{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DweYe9FcbMK_"
      },
      "source": [
        "##### Copyright 2019 The TensorFlow Authors.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "cellView": "form",
        "id": "AVV2e0XKbJeX"
      },
      "outputs": [],
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sUtoed20cRJJ"
      },
      "source": [
        "# 加载 CSV 数据"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ap_W4aQcgNT"
      },
      "source": [
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>     <a target=\"_blank\" href=\"https://tensorflow.google.cn/tutorials/load_data/csv\"><img src=\"https://tensorflow.google.cn/images/tf_logo_32px.png\">在 TensorFlow.org 上查看</a> </td>\n",
        "  <td><a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/zh-cn/tutorials/load_data/csv.ipynb\"><img src=\"https://tensorflow.google.cn/images/colab_logo_32px.png\">在 Google Colab 中运行</a></td>\n",
        "  <td><a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/zh-cn/tutorials/load_data/csv.ipynb\"><img src=\"https://tensorflow.google.cn/images/GitHub-Mark-32px.png\">在 Github 上查看源代码</a></td>\n",
        "  <td><a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/zh-cn/tutorials/load_data/csv.ipynb\"><img src=\"https://tensorflow.google.cn/images/download_logo_32px.png\">下载此 notebook</a></td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C-3Xbt0FfGfs"
      },
      "source": [
        "本教程提供了如何在 TensorFlow 中使用 CSV 数据的示例。\n",
        "\n",
        "其中包括两个主要部分：\n",
        "\n",
        "1. **从磁盘加载数据**\n",
        "2. **将数据预处理为适合训练的形式。**\n",
        "\n",
        "本教程侧重于加载，并提供了一些关于预处理的快速示例。要了解有关预处理方面的更多信息，请查看[使用预处理层](https://tensorflow.google.cn/guide/keras/preprocessing_layers)指南和[使用 Keras 预处理层对结构化数据进行分类](../structured_data/preprocessing_layers.ipynb)教程。\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fgZ9gjmPfSnK"
      },
      "source": [
        "## 安装"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "baYFZMW_bJHh"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Make numpy values easier to read.\n",
        "np.set_printoptions(precision=3, suppress=True)\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ZhJYbJxHNGJ"
      },
      "source": [
        "## 内存数据"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ny5TEgcmHjVx"
      },
      "source": [
        "对于任何较小的 CSV 数据集，在其上训练 TensorFlow 模型的最简单方式是将其作为 Pandas Dataframe 或 NumPy 数组加载到内存中。\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LgpBOuU8PGFf"
      },
      "source": [
        "一个相对简单的示例是 [Abalone Dataset](https://archive.ics.uci.edu/ml/datasets/abalone)。\n",
        "\n",
        "- 数据集很小。\n",
        "- 所有输入特征都是有限范围的浮点值。\n",
        "\n",
        "以下是将数据下载到 [Pandas `DataFrame`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html) 的方式："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "IZVExo9DKoNz",
        "outputId": "4a5ee974-12e3-412b-a280-26e607c43e01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Length  Diameter  Height  Whole weight  Shucked weight  Viscera weight  \\\n",
              "0   0.435     0.335   0.110         0.334          0.1355          0.0775   \n",
              "1   0.585     0.450   0.125         0.874          0.3545          0.2075   \n",
              "2   0.655     0.510   0.160         1.092          0.3960          0.2825   \n",
              "3   0.545     0.425   0.125         0.768          0.2940          0.1495   \n",
              "4   0.545     0.420   0.130         0.879          0.3740          0.1695   \n",
              "\n",
              "   Shell weight  Age  \n",
              "0        0.0965    7  \n",
              "1        0.2250    6  \n",
              "2        0.3700   14  \n",
              "3        0.2600   16  \n",
              "4        0.2300   13  "
            ],
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              "      <th>Length</th>\n",
              "      <th>Diameter</th>\n",
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              "      <th>0</th>\n",
              "      <td>0.435</td>\n",
              "      <td>0.335</td>\n",
              "      <td>0.110</td>\n",
              "      <td>0.334</td>\n",
              "      <td>0.1355</td>\n",
              "      <td>0.0775</td>\n",
              "      <td>0.0965</td>\n",
              "      <td>7</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.585</td>\n",
              "      <td>0.450</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.874</td>\n",
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              "      <td>6</td>\n",
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              "      <th>2</th>\n",
              "      <td>0.655</td>\n",
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              "      <td>0.3960</td>\n",
              "      <td>0.2825</td>\n",
              "      <td>0.3700</td>\n",
              "      <td>14</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.545</td>\n",
              "      <td>0.425</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.768</td>\n",
              "      <td>0.2940</td>\n",
              "      <td>0.1495</td>\n",
              "      <td>0.2600</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.545</td>\n",
              "      <td>0.420</td>\n",
              "      <td>0.130</td>\n",
              "      <td>0.879</td>\n",
              "      <td>0.3740</td>\n",
              "      <td>0.1695</td>\n",
              "      <td>0.2300</td>\n",
              "      <td>13</td>\n",
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              "        const element = document.querySelector('#df-dacbff0f-5008-4c60-bfc0-ff32c901de0a');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      let quickchartButtonEl =\n",
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              "</div>\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "abalone_train",
              "summary": "{\n  \"name\": \"abalone_train\",\n  \"rows\": 3320,\n  \"fields\": [\n    {\n      \"column\": \"Length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1211637312978371,\n        \"min\": 0.075,\n        \"max\": 0.815,\n        \"num_unique_values\": 132,\n        \"samples\": [\n          0.64,\n          0.26,\n          0.575\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Diameter\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10011993587250158,\n        \"min\": 0.055,\n        \"max\": 0.65,\n        \"num_unique_values\": 110,\n        \"samples\": [\n          0.23,\n          0.3,\n          0.42\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.042708242545774634,\n        \"min\": 0.0,\n        \"max\": 1.13,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          0.22,\n          0.04,\n          0.015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Whole weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4911819792381134,\n        \"min\": 0.002,\n        \"max\": 2.8255,\n        \"num_unique_values\": 2145,\n        \"samples\": [\n          1.145,\n          0.499,\n          0.9415\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Shucked weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.22222323442145425,\n        \"min\": 0.001,\n        \"max\": 1.488,\n        \"num_unique_values\": 1409,\n        \"samples\": [\n          0.13,\n          0.4,\n          0.214\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Viscera weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11018177158931347,\n        \"min\": 0.0005,\n        \"max\": 0.76,\n        \"num_unique_values\": 845,\n        \"samples\": [\n          0.097,\n          0.071,\n          0.541\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Shell weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14026098949905888,\n        \"min\": 0.0015,\n        \"max\": 1.005,\n        \"num_unique_values\": 855,\n        \"samples\": [\n          0.3045,\n          0.1255,\n          0.2035\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 27,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          12,\n          4,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
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            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_0['Length'].plot(kind='hist', bins=20, title='Length')\n",
              "plt.gca().spines[['top', 'right',]].set_visible(False)"
            ],
            "text/html": [
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              "rkJggg==\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-95119085-b91e-40cc-8ac3-5163e1a8dc13\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-95119085-b91e-40cc-8ac3-5163e1a8dc13\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_1['Diameter'].plot(kind='hist', bins=20, title='Diameter')\n",
              "plt.gca().spines[['top', 'right',]].set_visible(False)"
            ],
            "text/html": [
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              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-8a1d91ac-a6f9-48b3-a65b-8a54389fb5bd\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-8a1d91ac-a6f9-48b3-a65b-8a54389fb5bd\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_2['Height'].plot(kind='hist', bins=20, title='Height')\n",
              "plt.gca().spines[['top', 'right',]].set_visible(False)"
            ],
            "text/html": [
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              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-3c20203d-0119-497f-8b1a-b113fef450fb\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-3c20203d-0119-497f-8b1a-b113fef450fb\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_3['Whole weight'].plot(kind='hist', bins=20, title='Whole weight')\n",
              "plt.gca().spines[['top', 'right',]].set_visible(False)"
            ],
            "text/html": [
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              "AwAAjESIAQAARiLEAAAAI/0/4MjSzCKkdbgAAAAASUVORK5CYII=\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-d2aa5f13-9299-4a48-86d0-aecfda8ec9a8\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-d2aa5f13-9299-4a48-86d0-aecfda8ec9a8\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7a8b356636a0>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">2-d distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_4.plot(kind='scatter', x='Length', y='Diameter', s=32, alpha=.8)\n",
              "plt.gca().spines[['top', 'right',]].set_visible(False)"
            ],
            "text/html": [
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              "4xBwAACAcf4f74Rm+rtFCuoAAAAASUVORK5CYII=\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-f27a8271-b22e-46fd-a1a0-4c0dbf23f04e\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-f27a8271-b22e-46fd-a1a0-4c0dbf23f04e\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_5.plot(kind='scatter', x='Diameter', y='Height', s=32, alpha=.8)\n",
              "plt.gca().spines[['top', 'right',]].set_visible(False)"
            ],
            "text/html": [
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              "KxExAADASv8POUzkl6WpgjgAAAAASUVORK5CYII=\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-f4f4611b-d9f4-4dc9-b1cd-c4ddd15fcee4\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-f4f4611b-d9f4-4dc9-b1cd-c4ddd15fcee4\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_6.plot(kind='scatter', x='Height', y='Whole weight', s=32, alpha=.8)\n",
              "plt.gca().spines[['top', 'right',]].set_visible(False)"
            ],
            "text/html": [
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              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-f363aa36-6508-45a0-a6bb-d85f385f253f\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-f363aa36-6508-45a0-a6bb-d85f385f253f\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_7.plot(kind='scatter', x='Whole weight', y='Shucked weight', s=32, alpha=.8)\n",
              "plt.gca().spines[['top', 'right',]].set_visible(False)"
            ],
            "text/html": [
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              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-6fd670cc-e354-42db-ac43-f01505fceb1e\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-6fd670cc-e354-42db-ac43-f01505fceb1e\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7a8b3569b640>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Values</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_8['Length'].plot(kind='line', figsize=(8, 4), title='Length')\n",
              "plt.gca().spines[['top', 'right']].set_visible(False)"
            ],
            "text/html": [
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              "/PHHzVdffbVi7LJly8xOnTqZ4eHh5qBBg8zjx49bFbtOVHVuFi1aZHbr1q1iv7n33nvNU6dOWRm9\n",
              "VmVmZpoJCQlm9+7dzejoaPOKK66o+CQKdz/OVHVu3PFY81uXXnppxbvmXfk4o7XmRURERMQSemle\n",
              "RERERCyhIioiIiIillARFRERERFLqIiKiIiIiCVUREVERETEEiqiIiIiImIJFVERERERsYSKqIiI\n",
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              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-2a58a225-106e-4459-b435-b195279575b3\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-2a58a225-106e-4459-b435-b195279575b3\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_9['Diameter'].plot(kind='line', figsize=(8, 4), title='Diameter')\n",
              "plt.gca().spines[['top', 'right']].set_visible(False)"
            ],
            "text/html": [
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              "IiLic1RSRURERMTnqKSKiIiIiM/5f2wdlcp8lxy7AAAAAElFTkSuQmCC\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-e13ed5f4-eb79-4757-b59d-557671f65220\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-e13ed5f4-eb79-4757-b59d-557671f65220\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_10['Height'].plot(kind='line', figsize=(8, 4), title='Height')\n",
              "plt.gca().spines[['top', 'right']].set_visible(False)"
            ],
            "text/html": [
              "      <div class=\"colab-quickchart-chart-with-code\" id=\"chart-c62a42b6-acfb-4ffe-bbb2-8217deea4509\">\n",
              "        <img style=\"width: 180px;\" src=\"\n",
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              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-c62a42b6-acfb-4ffe-bbb2-8217deea4509\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-c62a42b6-acfb-4ffe-bbb2-8217deea4509\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_11['Whole weight'].plot(kind='line', figsize=(8, 4), title='Whole weight')\n",
              "plt.gca().spines[['top', 'right']].set_visible(False)"
            ],
            "text/html": [
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              "AElFTkSuQmCC\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-12465da4-5d5f-4267-ab23-871167947597\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-12465da4-5d5f-4267-ab23-871167947597\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "abalone_train = pd.read_csv(\n",
        "    \"https://storage.googleapis.com/download.tensorflow.org/data/abalone_train.csv\",\n",
        "    names=[\"Length\", \"Diameter\", \"Height\", \"Whole weight\", \"Shucked weight\",\n",
        "           \"Viscera weight\", \"Shell weight\", \"Age\"])\n",
        "\n",
        "abalone_train.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hP22mdyPQ1_t"
      },
      "source": [
        "该数据集包含一组[鲍鱼](https://en.wikipedia.org/wiki/Abalone)（一种海螺）的测量值。\n",
        "\n",
        "![an abalone shell](https://tensorflow.org/images/abalone_shell.jpg)\n",
        "\n",
        "[“鲍鱼壳”](https://www.flickr.com/photos/thenickster/16641048623/)（作者：[Nicki Dugan Pogue](https://www.flickr.com/photos/thenickster/)，CC BY-SA 2.0）\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vlfGrk_9N-wf"
      },
      "source": [
        "此数据集的名义任务是根据其他测量值预测年龄，因此要把特征和标签分开以进行训练：\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "udOnDJOxNi7p"
      },
      "outputs": [],
      "source": [
        "abalone_features = abalone_train.copy()\n",
        "abalone_labels = abalone_features.pop('Age')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "seK9n71-UBfT"
      },
      "source": [
        "对于此数据集，将以相同的方式处理所有特征。将这些特征打包成单个 NumPy 数组："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Dp3N5McbUMwb",
        "outputId": "9d0b57a1-0b38-4caa-c048-96a9af4d3262",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.435, 0.335, 0.11 , ..., 0.136, 0.077, 0.097],\n",
              "       [0.585, 0.45 , 0.125, ..., 0.354, 0.207, 0.225],\n",
              "       [0.655, 0.51 , 0.16 , ..., 0.396, 0.282, 0.37 ],\n",
              "       ...,\n",
              "       [0.53 , 0.42 , 0.13 , ..., 0.374, 0.167, 0.249],\n",
              "       [0.395, 0.315, 0.105, ..., 0.118, 0.091, 0.119],\n",
              "       [0.45 , 0.355, 0.12 , ..., 0.115, 0.067, 0.16 ]])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "abalone_features = np.array(abalone_features)\n",
        "abalone_features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1C1yFOxLOdxh"
      },
      "source": [
        "接下来，制作一个回归模型来预测年龄。由于只有一个输入张量，这里使用 `tf.keras.Sequential` 模型就足够了。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "d8zzNrZqOmfB"
      },
      "outputs": [],
      "source": [
        "abalone_model = tf.keras.Sequential([\n",
        "  layers.Dense(64, activation='relu'),\n",
        "  layers.Dense(1)\n",
        "])\n",
        "\n",
        "abalone_model.compile(loss = tf.keras.losses.MeanSquaredError(),\n",
        "                      optimizer = tf.keras.optimizers.Adam())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j6IWeP78O2wE"
      },
      "source": [
        "要训练该模型，请将特征和标签传递给 `Model.fit`："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "uZdpCD92SN3Z",
        "outputId": "6ef05ac5-c321-418a-f1ef-a8a52991588c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "104/104 [==============================] - 4s 7ms/step - loss: 82.0831\n",
            "Epoch 2/10\n",
            "104/104 [==============================] - 0s 4ms/step - loss: 26.2455\n",
            "Epoch 3/10\n",
            "104/104 [==============================] - 1s 5ms/step - loss: 8.8854\n",
            "Epoch 4/10\n",
            "104/104 [==============================] - 1s 5ms/step - loss: 8.2927\n",
            "Epoch 5/10\n",
            "104/104 [==============================] - 0s 4ms/step - loss: 7.9194\n",
            "Epoch 6/10\n",
            "104/104 [==============================] - 0s 4ms/step - loss: 7.5838\n",
            "Epoch 7/10\n",
            "104/104 [==============================] - 0s 5ms/step - loss: 7.2914\n",
            "Epoch 8/10\n",
            "104/104 [==============================] - 0s 4ms/step - loss: 7.0276\n",
            "Epoch 9/10\n",
            "104/104 [==============================] - 0s 3ms/step - loss: 6.7959\n",
            "Epoch 10/10\n",
            "104/104 [==============================] - 0s 3ms/step - loss: 6.6253\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7a8b39d1c040>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "abalone_model.fit(abalone_features, abalone_labels, epochs=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GapLOj1OOTQH"
      },
      "source": [
        "您刚刚看到了使用 CSV 数据训练模型的最基本方式。接下来，您将学习如何应用预处理来归一化数值列。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B87Rd1SOUv02"
      },
      "source": [
        "## 基本预处理"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yCrB2Jd-U0Vt"
      },
      "source": [
        "对模型的输入进行归一化是一种很好的做法。Keras 预处理层提供了一种便捷方式来将此归一化构建到您的模型。\n",
        "\n",
        "`tf.keras.layers.Normalization` 层会预先计算每列的均值和方差，并使用这些值对数据进行归一化。\n",
        "\n",
        "首先，创建层："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "H2WQpDU5VRk7"
      },
      "outputs": [],
      "source": [
        "normalize = layers.Normalization(input_dim=7)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hGgEZE-7Vpt6"
      },
      "source": [
        "然后，使用 `Normalization.adapt()` 方法使归一化层适应您的数据。\n",
        "\n",
        "注：仅将您的训练数据用于 `PreprocessingLayer.adapt` 方法。不要使用您的验证数据或测试数据。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "2WgOPIiOVpLg"
      },
      "outputs": [],
      "source": [
        "normalize.adapt(abalone_features)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rE6vh0byV7cE"
      },
      "source": [
        "然后，将归一化层用于您的模型："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "quPcZ9dTWA9A",
        "outputId": "6aa6f1c3-3cb9-4681-f78e-83ceebda1baf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "104/104 [==============================] - 1s 2ms/step - loss: 80.2606\n",
            "Epoch 2/10\n",
            "104/104 [==============================] - 0s 2ms/step - loss: 41.6508\n",
            "Epoch 3/10\n",
            "104/104 [==============================] - 0s 2ms/step - loss: 25.1210\n",
            "Epoch 4/10\n",
            "104/104 [==============================] - 0s 2ms/step - loss: 15.8250\n",
            "Epoch 5/10\n",
            "104/104 [==============================] - 0s 2ms/step - loss: 9.8845\n",
            "Epoch 6/10\n",
            "104/104 [==============================] - 0s 2ms/step - loss: 7.5945\n",
            "Epoch 7/10\n",
            "104/104 [==============================] - 0s 2ms/step - loss: 6.7684\n",
            "Epoch 8/10\n",
            "104/104 [==============================] - 0s 2ms/step - loss: 6.3015\n",
            "Epoch 9/10\n",
            "104/104 [==============================] - 0s 2ms/step - loss: 5.9336\n",
            "Epoch 10/10\n",
            "104/104 [==============================] - 0s 2ms/step - loss: 5.6641\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7a8b35869f30>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "norm_abalone_model = tf.keras.Sequential([\n",
        "  normalize,\n",
        "  layers.Dense(64, activation='relu'),\n",
        "  layers.Dense(1)\n",
        "])\n",
        "\n",
        "norm_abalone_model.compile(loss = tf.keras.losses.MeanSquaredError(),\n",
        "                           optimizer = tf.keras.optimizers.Adam())\n",
        "\n",
        "norm_abalone_model.fit(abalone_features, abalone_labels, epochs=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 保存模型"
      ],
      "metadata": {
        "id": "iBhTu9_7XtJs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "norm_abalone_model.save('norm_abalone_model.keras')"
      ],
      "metadata": {
        "id": "sbcMNMjBXRp6"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 加载模型"
      ],
      "metadata": {
        "id": "qfeezd3qXy8h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.models.load_model('norm_abalone_model.keras')"
      ],
      "metadata": {
        "id": "BB-taoaZXYVS"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wuqj601Qw0Ml"
      },
      "source": [
        "## 混合数据类型\n",
        "\n",
        "\"Titanic\" 数据集包含有关泰坦尼克号乘客的信息。该数据集的名义任务是预测幸存者。\n",
        "\n",
        "![交通堵塞。](https://github.com/tensorflow/docs-l10n/blob/master/site/zh-cn/tutorials/load_data/images/csv/traffic.jpg?raw=1)\n",
        "\n",
        "图片[来自 Wikimedia](https://commons.wikimedia.org/wiki/File:Trafficjam.jpg)\n",
        "\n",
        "原始数据可以轻松加载为 Pandas `DataFrame`，但不能立即用作 TensorFlow 模型的输入。\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "GS-dBMpuYMnz",
        "outputId": "cf9c10e8-f61c-4811-d5bf-fca096d13b28",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   survived     sex   age  n_siblings_spouses  parch     fare  class     deck  \\\n",
              "0         0    male  22.0                   1      0   7.2500  Third  unknown   \n",
              "1         1  female  38.0                   1      0  71.2833  First        C   \n",
              "2         1  female  26.0                   0      0   7.9250  Third  unknown   \n",
              "3         1  female  35.0                   1      0  53.1000  First        C   \n",
              "4         0    male  28.0                   0      0   8.4583  Third  unknown   \n",
              "\n",
              "   embark_town alone  \n",
              "0  Southampton     n  \n",
              "1    Cherbourg     n  \n",
              "2  Southampton     y  \n",
              "3  Southampton     n  \n",
              "4   Queenstown     y  "
            ],
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "\n",
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              "    async function quickchart(key) {\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "titanic",
              "summary": "{\n  \"name\": \"titanic\",\n  \"rows\": 627,\n  \"fields\": [\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.511817629565815,\n        \"min\": 0.75,\n        \"max\": 80.0,\n        \"num_unique_values\": 76,\n        \"samples\": [\n          28.0,\n          59.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"n_siblings_spouses\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 54.5977304994563,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 216,\n        \"samples\": [\n          25.9292,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Third\",\n          \"First\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deck\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"C\",\n          \"D\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embark_town\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Cherbourg\",\n          \"unknown\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alone\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"y\",\n          \"n\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "titanic = pd.read_csv(\"https://storage.googleapis.com/tf-datasets/titanic/train.csv\")\n",
        "titanic.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "D8rCGIK1ZzKx"
      },
      "outputs": [],
      "source": [
        "titanic_features = titanic.copy()\n",
        "titanic_labels = titanic_features.pop('survived')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "urHOwpCDYtcI"
      },
      "source": [
        "由于数据类型和范围不同，您不能简单地将特征堆叠到 NumPy 数组中并将其传递给 `tf.keras.Sequential` 模型。每列都需要单独处理。\n",
        "\n",
        "作为一种选择，您可以（使用您喜欢的任何工具）离线预处理数据，将分类列转换为数值列，然后将处理后的输出传递给 TensorFlow 模型。这种方式的缺点是，如果保存并导出模型，预处理不会随之保存。Keras 预处理层能够避免这个问题，因为它们是模型的一部分。\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bta4Sx0Zau5v"
      },
      "source": [
        "在此示例中，您将构建一个使用 [Keras 函数式 API](https://tensorflow.google.cn/guide/keras/functional) 实现预处理逻辑的模型。您也可以通过[子类化](https://tensorflow.google.cn/guide/keras/custom_layers_and_models)来实现。\n",
        "\n",
        "函数式 API 会对“符号”张量进行运算。正常的 \"eager\" 张量有一个值。相比之下，这些“符号”张量则没有值。相反，它们会跟踪在它们上面运行的运算，并构建可以稍后运行的计算的表示。以下是一个简单示例："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "730F16_97D-3",
        "outputId": "716ea59d-761e-4c22-8d26-e011d762f209",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<KerasTensor: shape=(None,) dtype=float32 (created by layer 'tf.__operators__.add')>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "# Create a symbolic input\n",
        "input = tf.keras.Input(shape=(), dtype=tf.float32)\n",
        "\n",
        "# Perform a calculation using the input\n",
        "result = 2*input + 1\n",
        "\n",
        "# the result doesn't have a value\n",
        "result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "RtcNXWB18kMJ"
      },
      "outputs": [],
      "source": [
        "calc = tf.keras.Model(inputs=input, outputs=result)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "fUGQOUqZ8sa-",
        "outputId": "b082c56e-2102-49ef-ceb7-6e7d4baf8049",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3.0\n",
            "5.0\n"
          ]
        }
      ],
      "source": [
        "print(calc(1).numpy())\n",
        "print(calc(2).numpy())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rNS9lT7f6_U2"
      },
      "source": [
        "要构建预处理模型，首先要构建一组符号 `tf.keras.Input` 对象，匹配 CSV 列的名称和数据类型。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "5WODe_1da3yw",
        "outputId": "c349d393-1d32-4d1a-d649-d1b09877a967",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'sex': <KerasTensor: shape=(None, 1) dtype=string (created by layer 'sex')>,\n",
              " 'age': <KerasTensor: shape=(None, 1) dtype=float32 (created by layer 'age')>,\n",
              " 'n_siblings_spouses': <KerasTensor: shape=(None, 1) dtype=float32 (created by layer 'n_siblings_spouses')>,\n",
              " 'parch': <KerasTensor: shape=(None, 1) dtype=float32 (created by layer 'parch')>,\n",
              " 'fare': <KerasTensor: shape=(None, 1) dtype=float32 (created by layer 'fare')>,\n",
              " 'class': <KerasTensor: shape=(None, 1) dtype=string (created by layer 'class')>,\n",
              " 'deck': <KerasTensor: shape=(None, 1) dtype=string (created by layer 'deck')>,\n",
              " 'embark_town': <KerasTensor: shape=(None, 1) dtype=string (created by layer 'embark_town')>,\n",
              " 'alone': <KerasTensor: shape=(None, 1) dtype=string (created by layer 'alone')>}"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "inputs = {}\n",
        "\n",
        "for name, column in titanic_features.items():\n",
        "  dtype = column.dtype\n",
        "  if dtype == object:\n",
        "    dtype = tf.string\n",
        "  else:\n",
        "    dtype = tf.float32\n",
        "\n",
        "  inputs[name] = tf.keras.Input(shape=(1,), name=name, dtype=dtype)\n",
        "\n",
        "inputs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aaheJFmymq8l"
      },
      "source": [
        "预处理逻辑的第一步是将数值输入串联在一起，并通过归一化层运行它们："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "wPRC_E6rkp8D",
        "outputId": "f344869f-bd1b-4fe1-91a8-5a3450777fe2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<KerasTensor: shape=(None, 4) dtype=float32 (created by layer 'normalization_1')>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "numeric_inputs = {name:input for name,input in inputs.items()\n",
        "                  if input.dtype==tf.float32}\n",
        "\n",
        "x = layers.Concatenate()(list(numeric_inputs.values()))\n",
        "norm = layers.Normalization()\n",
        "norm.adapt(np.array(titanic[numeric_inputs.keys()]))\n",
        "all_numeric_inputs = norm(x)\n",
        "\n",
        "all_numeric_inputs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-JoR45Uj712l"
      },
      "source": [
        "收集所有符号预处理结果，稍后将它们串联起来："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "M7jIJw5XntdN"
      },
      "outputs": [],
      "source": [
        "preprocessed_inputs = [all_numeric_inputs]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r0Hryylyosfm"
      },
      "source": [
        "对于字符串输入，请使用 `tf.keras.layers.StringLookup` 函数将字符串映射到词汇表中的整数索引。接下来，使用 `tf.keras.layers.CategoryEncoding` 将索引转换为适合模型的 `float32` 数据。\n",
        "\n",
        "`tf.keras.layers.CategoryEncoding` 层的默认设置会为每个输入创建一个独热向量。也可以使用 `tf.keras.layers.Embedding`。请参阅[使用预处理层](https://tensorflow.google.cn/guide/keras/preprocessing_layers)指南和[使用 Keras 预处理层对结构化数据进行分类](../structured_data/preprocessing_layers.ipynb)教程，了解有关此主题的更多信息。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "79fi1Cgan2YV"
      },
      "outputs": [],
      "source": [
        "for name, input in inputs.items():\n",
        "  if input.dtype == tf.float32:\n",
        "    continue\n",
        "\n",
        "  lookup = layers.StringLookup(vocabulary=np.unique(titanic_features[name]))\n",
        "  one_hot = layers.CategoryEncoding(num_tokens=lookup.vocabulary_size())\n",
        "\n",
        "  x = lookup(input)\n",
        "  x = one_hot(x)\n",
        "  preprocessed_inputs.append(x)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wnhv0T7itnc7"
      },
      "source": [
        "您可以使用 `inputs` 和 `processed_inputs` 的集合将所有预处理的输入串联在一起，并构建处理预处理的模型："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "XJRzUTe8ukXc",
        "outputId": "a7703c5a-eb1c-4ff3-c4da-a417259564fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 396
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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9fHyEENOnT3dxcUlISIiIiHBycho1atTFixdv3LjRrVu39evX1997SEjII488kpOT8/rrrz/77LO5ubn65adOnRo/frzpVOrk5BQaGlqn2LCwMNOsFAAAAADQNiRj3eosPDy85t/6lEplUlLS008/7ezsLIT44x//eOvWrdWrVxtl1/VVVlZOnTrV09NzxYoVhqx///59Dw+P3bt3u7u7X7169cMPPywpKdmyZUuDK1MpAAAAAEAubXc13sfHp2vXrmfPnhVCLF269NKlSz/88EMr7SsuLs7Z2TkkJMTA9cPCwpYvX+7h4bFy5Uo3N7fY2NiW7L3jVAoAAAAAaGNtdDW+vsrKyj/96U8BAQG9evUySoBmS0pKsre3Hzt2rIHrUykAAAAAQC6Wcu3Yyspq+fLlcu29tkmTJrXq+B2nUgAAAABAa5PhFncAAAAAAKB5aOPRmOLiYrkjAAAAAAD+gzYeD1RdXb1ly5bx48cfP35c7iwAAAAAACGMe4u7iIgIowwF0xEWFubq6rpy5cq+fft+8MEHw4cPlzsRAAAAAHRoRmvj0Y5VVFRs2bIlMjLSw8MjKipq2LBhcicCAAAAgA6KN9Xj4Tp37jx79uxLly69/vrrEyZM8PX15W32AAAAACAL2ngYSt/MX7x40dfXd/z48b6+vidOnJA7FAAAAAB0LLTxaBpra+vZs2efO3du5MiRL7/8cmBg4KVLl+QOBQAAAAAdBW08msPW1nbx4sUXLlwYOHDg008/HRwcfO3aNblDAQAAAED7RxuP5rOzswsNDT179qxKpRo4cODSpUtv374tdygAAAAAaM9o49FSTk5OUVFRJ06cKCwsdHd3j46OLi8vlzsUAAAAALRPtPEwDldX140bN2ZkZPzrX/9yd3fftGlTVVWV3KEAAAAAoL3he+NhfIcPH9a/wT4qKurll1+WOw4AAAAAtB+08WgtqampoaGhrq6uMTEx/fv3lzsOAAAAALQHvKkercXX1/fkyZMTJ0709vYOCgrKy8uTOxEAAAAAmD3aeLQiS0vL2bNnnzlzpkePHv379w8PD+fudwAAAADQErTxaHUqlSoqKurIkSNnzpx54oknEhIS+CgHAAAAADQPn41Hm9q/f39ISIiNjc1HH330zDPPyB0HAAAAAMwMV+PRpkaNGnX8+PE5c+a89tpr/v7+ly9fljsRAAAAAJgT2ni0NQsLi6CgoPPnz/fr12/o0KHh4eFlZWVyhwIAAAAA80AbD3nY2dmFh4efOHHi0qVL7u7ufGAeAAAAAAzBZ+Mhvx9++GHRokX379+PiYkZMWKE3HEAAAAAwHT952p8eHi4BBhVeHi4IWfh008/ffjw4XfffXfatGn+/v7/+7//20qnOwAAAACYu/96U71Wq9Wh9Wm12o4w1Vqt1vATUZKk119//ZdffvHw8Bg+fDgfmAcAAACABvHZeJgQOzu7iIiIEydOZGZm9u/f/8svv5Q7EQAAAACYFtp4mBxXV9ekpKT4+PiwsDBvb+8zZ87InQgAAAAATAVtPEzUyJEjT5w44evr6+XltWDBguLiYrkTAQAAAID8aONhuiwtLRcsWHDq1KnCwkIPD4+EhAS5EwEAAACAzFq9jR87duyGDRuEEP369ZMk6caNGzVPLV68uHPnzmFhYXXW7Mg+++wzlUolSVKfPn0uX75slDFjYmKUSqUkSb179z569KhRxmxLGo0mISFh586d69ev/93vfvfLL7/InQgAAAAAZGPZ2jvYs2eP/ofvv/9epVLVfmr16tU3b96sv2ZH9sYbbzg4OLz66qsXLlxo+Wharfatt94KCQnp3LnzqlWrLl682PIx5fLkk08ePXr0888/Hz16tL+//8qVK+3t7eUOBQAAAABtra3fVG9lZWWUcY4ePXr69GmjDNWOJSUlyR3BmCwsLIKCgvTHvV+/fgkJCTqdTu5QAAAAANCmDGrjQ0NDJUmaM2eOh4eHUqmMjIx80JoVFRX+/v52dnbOzs6bN2+Oj49XKBQ1b5sXQgwcONDa2rpPnz7bt2+vvWHtNR+0u6+//rpv374KhcLV1XXZsmW9evWqvS9D8gghQkJCJEkaPXq0Uqns1atXcnKyECI9PX3w4MFKpdLT03Pv3r1CCH9/f0mSLly4kJub6+bmplQqGxwtJSXF3d3dwcFhxowZFRUV9VdoiQbnocH8DaadNGlSVlaWq6vrvHnzGtnL7NmzVSqVjY3NtGnTqqurx40bJ0mSm5tbbm7uF1984eDg0K9fvzplzp8/X5KkPXv2+Pn5LV++vIVlNpWjo+OaNWu+/PLLTz75xMvL6+TJk20cAAAAAABkZFAbHx0drVarp06deubMmQ0bNkRHRz9ozZ07d965c+fGjRvffffdzZs3Z8yYERgYWHuFr7/++vbt26GhoW+88cavv/5as7z2mg3u7t69ewEBAf/zP/9z69Ytf3//8vLy1NTU2vsyJI8QIiYmxs7ObsmSJQUFBUuXLg0KCvrll19eeeWVJUuWXLt2be7cuRMnTszPz//73/+uH6FHjx67du1qcLTr168HBgbGxMRkZ2efPHly48aN9XfXEg3OQ/38169fbzDt1q1bhRBXrlxZv359I3tRKBSZmZmnTp1KSkrKzMxMTk52cHBYs2ZNjx49Jk6cGBAQsG3btjplrlu3Tq1W37x589NPP3VwcGhhmc0zbNiw77//ftasWS+99FJQUFDLZxsAAAAAzELT3lQvSZKXl1dpaen9+/cbXEGpVB4/fnzfvn19+/b9wx/+UH+F7t27KxSKt956y8XF5bvvvjN8d1euXCkuLp4wYYKtre3LL7988eLFh+6rkTzdu3e3sbEJDg52cnJasWKFWq2eMmVKly5d9Ev2799vyGgZGRmurq4+Pj6Ojo7jx48/cOCAIZGaof60186fkZHRksHXrl2r0Wj69Onj6OhYXFxsa2urb92FEJWVlZWVlWfPnq1Tpn5DNzc3BweH0NDQlhXXfJIkBQUFnTlzRqVS9evXb82aNdXV1XKFAQAAAIC2YeTPxr/88ssLFy58++233dzc0tPTG1nTycmpqKjI8JE1Go1Cofjyyy/Lysq+/vrrfv36GbKvh67TrVu3M2fOdO3atWaJWq3Oy8szZLSCgoJz585JkiRJklarLSoqMrx8Y+nWrVtLLkQXFxdPnjzZ2dm5c+fONVXPnDkzNTW1uLh43759r732Wv0yjZTdOFQq1Zo1a7755pu///3vzz333PHjx+VOBAAAAACtyMhtvCRJy5cvv3r16qxZsxr5SLZOp7ty5Yqrq6vhIyuVyqioqLfeekulUh05ciQ2NtaQfTW+jk6ny8nJGTZsWEFBQc3C69evazQaQ0ZTqVQDBw7U/VtaWpqB5RuLPn/Pnj2bPUJCQkJmZuZPP/1UVlamVqv1C4cPH+7u7r5z587Dhw+PGTOmfplGim9MQ4YMOXTo0Ntvv+3j47Nw4cLi4mK5EwEAAABAqzByG/+Xv/wlLS2tqqrqySeflCSp/gplZWXl5eVr166trKwcPXq04SOXlpYmJSX98ssv5eXlR44c6dev30P31Uieu3fvlpeXr1u3rqKi4v3338/Pz9+6dWtxcXFcXFxhYaG3t7cQQqlUHj58uLKyMicnp8HRRo4cmZWVtW3btpKSktLS0qKiIkMiGUXt/KNGjWowrYWFhYWFRWZmZmlpae1tdTpdUVFRcHCwEOLevXvW1tZKpTIrK6u8vLxmnZkzZ27evNnR0bFTp071y2y9ulpCkqQ33njjzJkzOp3Ow8MjISFB7kQAAAAA0ApqrrJqtVqtVqtryJIlS4QQjz76aFFRUf/+/YUQU6dObXDN1NTUHj16WFpauru7p6WlhYaGdu7c2dbWNjo6uqys7KWXXnJycrK2th42bNjhw4f/8Ic/WFlZ2dra/vnPf669ZoO7Ky8v/+1vf6vPLEnSY4899oc//KH2vgzJo19oZ2fn6OhoZWXl6emZkZGh0+n27ds3cOBAW1tbT0/PmtXee+89hULh7u6u73jffPPN+qP97W9/c3d3t7a2fvrpp3/88ccGd1dfI1OdkJCgUqmEEI8//nhAQECD014/f4NpdTqdv7+/tbW1EEJ/7/rapk+frtPpfv311yeeeMLOzi4gIKB37969e/euqqrS6XS3bt2ytbXNzs7WD16nzPnz5wshunfvfvjw4QfV2HiZre3QoUMDBw58+eWXL126JEsAAAAAAGglku7f37wdHh5e868Junnz5uLFi//yl79YWVlVVVVFRkaeOnUqJSWlGUMplcpjx47169fP6CEN1MKpboP8Op1u4cKFa9asackg8p5R9+/f37Bhw/vvvz9nzpxly5bpX84AAAAAAHPXzDfVX716VWrI1atXjZuvxrfffnv58uWioqKKioqsrKwDBw4MGTKk2ZHM/ZbmrZf/4MGDJSUl4eHhEyZMaKVdtA1LS8sFCxYcP378l19+GTRo0Lfffit3IgAAAAAwgma28S4uLg1e3HdxcTFuvhrjxo1Tq9X6d4C/9NJLzz333NKlS5sRadq0aSUlJWPGjDHTW5q3dv7Y2FiNRiNJkv4j9+auZ8+eO3bs+POf/zxr1qygoKDa9zIEAAAAAHNkKXcAQ9nZ2W3fvr3l4yQmJiYmJrZ8HLm0dn6jTLKp8fX1/f3vfx8ZGdm/f/9Vq1a99dZbrXoDQgAAAABoPUa+Uz1gmmxtbaOiotLS0jZv3uzl5XX69Gm5EwEAAABAc9DGowPx9PT8/vvvZ82a5e3tvWDBgpKSErkTAQAAAEDT0MajY5EkKSgo6MSJE4WFhYMGDdqzZ4/ciQAAAACgCf7rC+ciIiLkTYN2RqvVmuxXGAohMjIy5syZ06dPn9jYWFdXV7njAAAAAMDD/dfVeK1W21rfT2/mtFotk9NUWq1WrtPaQCNHjjx+/PjgwYOHDRsWGxtr7l9DCAAAAKAj4E316NBsbGxWrlx54MCB7du3v/DCC5mZmXInAgAAAIDG0MYDom/fvgcOHJg9e/bvfve7pUuX3rt3T+5EAAAAANAw2nhAiFq3vrt48eLAgQMzMjLkTgQAAAAADaCNB/6je/fuycnJq1evDgoKCg4OvnPnjtyJAAAAAOC/tGkbP3bs2A0bNrTlHoFm8PX1PXXqlEKh6NevX0pKitxxAAAAAOA/2rSN37NnzzvvvGOUobRa7dWrV40ylDlqRvkdfMaaysHBYc2aNX/7299WrFjh6+ubk5MjdyIAAAAAEMJ831SflJQkdwQ5NaP8Dj5jzTNixIgTJ04MGzZs6NChmzZt0ul0cicCAAAA0NG1XRsfHx+vUCjCwsKEEKGhoZIkzZkzx8PDQ6lURkZGCiFCQkIkSRo9erRSqezVq1dycrIQwt/fX5KkCxcu5Obmurm5KZVKIcSkSZOysrJcXV3nzZvn6+u7aNGiNquiVaWnpw8ePFipVHp6eu7du1c8rHxJkpoxY0KI9jRprU2hUISHh6enp8fHx48cOTIrK0vuRAAAAAA6Nt2/abVarVara00zZ85csWKF/me1Wn3w4MHq6uotW7bY2trqF9rZ2e3bt6+0tDQuLk6hUFy7dk1//fP8+fM6ne7EiRN2dnY6na6yslIIceXKlVZNW1sbTE5BQYGdnd3WrVvv3LkTFxdnZ2eXl5ene1j5JjtjujaZtLZUVVW1cePGrl27arXae/fuyR0HAAAAQAcl85vqJUny8vIqLS29f/++fkn37t1tbGyCg4OdnJw61Jd+paWlqdXqKVOmdOnSRV/+/v37Ddmww85YG7OwsJg9e/axY8eOHj365JNP/vOf/5Q7EQAAAICOyHQ/G9+tW7ebN2/KnaLt5OXlde3ateahWq3Oy8tr0ggdbcZk4ebm9s0334SFhU2YMGHBggV3796VOxEAAACAjsVE23idTpeTk9OzZ0+5g7QdjUZTUFBQ8/D69esajcbwzTvgjMno9ddfP3ny5K1btzw9PQ180wQAAAAAGIXJtfF37/l4xQgAACAASURBVN4tLy9ft25dRUXFqFGjhBBKpfLw4cOVlZU1X/plYWFhYWGRmZlZWloqa1hj8vb2zs/P37p1a3FxcVxcXGFhobe3tzCg/A47Y/JydnZOTExcv379m2+++fbbb9+5c0fuRAAAAAA6hLZr45cuXZqYmPjRRx99+OGHoaGheXl5gYGBt2/f9vHxEUJMnz5dv9q4cePs7e3j4+N37dplb28vhFi0aNHbb789YMCA1NTUkpKSGTNmWFhY+Pn5+fr6zpo1y8fHZ+HChW1WRetxdnZOSUmJjo7WaDRxcXE7d+50cnISDytfNH3GhBDtZtJkN3bs2FOnTllbW/fr1+/LL7+UOw4AAACA9k/S/fursMPDw2v+lYtSqTx27Fi/fv1kzNAgU5icBpnsjAkTnrTWcPDgwZkzZw4ePPiTTz7Rv/4CAAAAAK3B5N5UX11dLXcEM8OMmYLnn3/+559/fuyxxwYNGvTFF1/IHQcAAABAu2VCbfy0adNKSkrGjBlz/PhxubOYB2bMpNjY2ERFRSUnJ4eFhfn7+9e+YSEAAAAAGIsJtfGJiYk6ne7q1atDhw6VO4t5YMZM0LPPPnvixInHHnusf//+mzZtkjsOAAAAgPbGhNp4oH1QKBRRUVH79u2Li4vz8fGp+b4AAAAAAGg52nigVQwePPiHH354/vnnhw4dymV5AAAAAMZCGw+0Fisrq9DQ0LS0tE2bNo0dO/bKlStyJwIAAABg9v7rC+ciIiLkTYN2RqvVdpAvnGvc/fv3V69evXr16hUrVsyfP9/CgpfPAAAAADSTaX1vvFlgogzERNVx+vTpGTNmWFtbf/rpp48//rjccQAAAACYJa4KAm2kf//+33//vY+Pz7PPPhsbG1vzChoAAAAAGI42Hmg7nTp1WrJkycGDBxMSEkaPHv3rr7/KnQgAAACAmaGNB9pa3759v//+e19f3yeffHLTpk1clgcAAABgONp4QAYWFhYLFizIyMj461//OmbMGG5iDwAAAMBARmvjlUqlJEmSJJ09e1YIUVlZGRUVlZ2dHRMTo38qJiZGv+ahQ4dcXFw6d+785ptvGmvvtWVmZo4aNeqbb77RP9yxY8eePXtqng0PD9fnnD59emvs/aGYKNTo16/f999/P2rUqGHDhvHd8gAAAAAMYcyr8ampqfn5+X379q2qqvL39//973/fq1evkJCQqKgoNze3VatW3bp1SwgxYsSIo0ePBgUFbd682Yh719u+fXtCQsLPP/9cs8TPz+/QoUPx8fH6h2FhYdnZ2XPnzjX6rg3HRKGGpaVlaGjo/v379d8tn5OTI3ciAAAAACbNmG38gAEDunbtKoR4//33u3Xr9uSTT9Y8FRkZqVAoVq5cacTdNSggIOCDDz6wsbGpvTAsLCw8PPzMmTNCCEtLSzc3N1dX19ZO0ggmCnUMGDDg6NGjI0eOHDp0KJflAQAAADTC+J+Nr6qqiouLCwoKqr1QpVJ98sknsbGxFy9erL08PT198ODBSqXS09Nz7969QojQ0FBJkubMmePh4aFUKiMjI/VrpqSkuLu7Ozg4zJgxo6KiokmRbGxsXnnlFVPrjpgo1Ka/LJ+WlhYXF+fj45Obmyt3IgAAAACmyPht/I8//pibmzto0KA6yydMmPDqq68uXbq0ZsmNGzdeeeWVJUuWXLt2be7cuRMnTszPz4+Ojlar1VOnTj1z5syGDRuio6OFENevXw8MDIyJicnOzj558uTGjRubmmrIkCG7d+9uYWnGxUShvkGDBv3www/PP//8kCFDEhMT5Y4DAAAAwOQYv43Pzs62trbu0qVL/afWrVv33XffHTlyRP8wLS1NrVZPmTKlS5cuwcHBTk5O+/fvr1lZkiQvL6/S0tL79+9nZGS4urr6+Pg4OjqOHz/+wIEDTU3l7Oz866+/mtQ3ezFRaJCVlVVoaGhqauoHH3zg7+9/48YNuRMBAAAAMCHGb+PLysqsra0bfKpr165r165dvHix/mFeXp7+I+J6arU6Ly+vwQ0LCgrOnTunv3G6VqstKipqaipra+vq6ury8vKmbth6mCg04qmnnjp+/Phjjz02aNCglJQUueMAAAAAMBXGb+NtbW0b+Uj25MmT1Wp1cnKyEEKj0RQUFNQ8df36dY1G0+BWKpVq4MCBun9LS0traqqKigoLCwuFQtHUDVsPE4XGKRSKqKio5OTkpUuXTps2rbCwUO5EAAAAAORn/Dbezc2tvLz87t27D1ohNjZ2w4YNQghvb+/8/PytW7cWFxfHxcUVFhZ6e3s3uMnIkSOzsrK2bdtWUlJSWlrajIvMBQUFjz76qCRJTd2w9TBRMMRzzz33008/OTo6Dho0SH93QwAAAAAdmfHb+OHDh/fo0eP06dP6hx9//PGyZcveeOONTz/9VL+ke/fuYWFhQghnZ+eUlJTo6GiNRhMXF7dz504nJ6fQ0NC8vLzAwMDbt2/7+PgIIaZPn+7i4pKQkBAREeHk5DRq1KiLFy/euHGjW7du69evr7P3kJCQRx55JCcn5/XXX3/22Wdrbvd96tSp8ePHG73YlmCiYCBbW9s1a9b87W9/mzdvXnBwcCMv/QAAAABo96Sam5mFh4fX/NsMSqUyKSnp6aefdnZ2/uMf/3jr1q3Vq1cbKWQDKisrp06d6unpuWLFioeufP/+fQ8Pj927d3t4eFRVVV29evXDDz8sKSnZsmVLM3bNRBmohROF+u7cufPuu++mpaVt2bLlhRdekDsOAAAAABkY82q8j49P165dz549u3Tp0kuXLv3www9GHLyOuLg4Z2fnkJAQQ1YOCwtbvny5h4eHEGLlypVubm6xsbGtl+2hOs5EFRUVlZWVtWQE1GZvb79x48b169dPmTJlwYIF9+7dkzsRAAAAgLZmtKvxdVRWVv7pT38KCAjo1auXUQZstqSkJHt7+7FjxxprQCbKQOHh4Xv27Dl79uzIkSPHjRs3bty4nj17GmvwDq6goCA4OPj8+fMJCQlDhgyROw4AAACAttOpph3NyMgQQowcOdI443bq9Pzzz6tUKqOM1hIDBgx4/PHHjTggE2WgjIyMxx9/PCUlRaVSfffdd4sXL968efOVK1cUCoWLiwt30WsJOzu7SZMmde3adcqUKSUlJc8//7yFhfHvcwEAAADABPG//mhddnZ2vr6+GzduvHr1akJCgkKhWLhwoUaj8ff3T0hIuH37ttwBzdjrr7/+z3/+8+DBg88///z58+fljgMAAACgLdDGo4106tRp2LBh4eHhP/7447Fjx7y9vZOTk3v27DlixIjo6OjMzEy5A5qlRx55JD09ffr06c8+++yaNWtqPiMDAAAAoL2ijYcM3NzcZs+enZqamp+fHx4enpubO3r06N69ey9YsCA9Pb2yslLugOZEkqTZs2cfOHDg888/Hzt2bE5OjtyJAAAAALSi/7rFXUREhLxp0M5otVrD7wV4+vTpr776KjU19ezZs6NGjfL29h4/frxGo2nNgO3K/fv3V69eHRMTs3r16sDAQLnjAAAAAGgVEu/ChakpKCjYs2fPV199lZaW1rt3bx8fH19f32HDhsmdyzycPHly2rRpTzzxxCeffOLk5CR3HAAAAABGRhsP01VeXn7o0KH09PRdu3bdu3fvxRdf9PHxefHFF62treWOZtLKysqWLVuWkpLy6aefjh49Wu44AAAAAIyJNh7m4dKlS6mpqV999dWxY8eeeuopHx8fPz8/voi+Ed9+++2bb7752muvRUVFKRQKueMAAAAAMA7aeJiZW7duffvtt/qWvnv37r6+vj4+Ps899xxfRF/f7du3582bd/z48cTExKFDh8odBwAAAIAR0MbDXFVVVR05cuSrr75KT0+/fPnymDFjfH19X3rpJXt7e7mjmZbk5OR58+bNmTPnvffe69Spk9xxAAAAALQIbTzag+zs7LS0tNTU1AMHDjz11FPe3t6vvPLKE088IXcuU3H58uXp06eXl5cnJib27t1b7jgAAAAAmo82Hu1KaWnpt99++9VXX3311VcKhUJ/l3svLy8rKyu5o8lMp9OtXbv2j3/846pVq2bPni13HAAAAADNRBuP9qm6uvrEiRPp6empqalZWVm/+93vfHx8xo8f/5vf/EbuaHI6ffr01KlTH3/88bi4OL6ODgAAADBHtPFo//Lz87/55hv9F9H379/f19fX29u7w34RfXl5eXh4eGJi4l/+8peXX365zrM6nY6bBQIAAACmjDYeHUhZWdnhw4dTU1N37txpZWXl7e3dYb+IXv91dGPHjv3oo49sbW31C3/88cfY2Nj4+Hh5swEAAABoBG08OqjTp0/r73J/7NixF154Qf/FdT169JA7V9u5ffv2O++8c+LEic8//3zIkCElJSV9+/bNz89PSEiYNGmS3OkAAAAANIw2Hh3djRs3/vGPf+i/iP6xxx7T3xVv6NChjb+3/LvvvvPy8mqzkK0nMTFx8eLF77777qlTp7744ovS0tIuXbr88ssvjzzyiNzRAAAAADSANh74PzVfRL979+6bN2++9NJLvr6+Y8eOVSqVddYsKytTqVReXl7btm1rBzeK+/XXX5cvX75r167S0lIhhKWl5aBBg44dO8aXzAMAAAAmiDYeaMClS5f01+ePHTv21FNP+fj4vPbaa66urvpnv/7664CAgHv37nXp0mXHjh0jR46UNWxL5efnu7u73759u2aJnZ3dsmXLVqxYIWMqAAAAAA2ijQcaU1JSsn//fv0lekdHR/1d7hMTEz///PPq6mohhK2t7RtvvPHxxx937txZ7rDNodPpRo4ceeTIkcrKytrLbW1tMzIynnzySbmCAQAAAGgQbTxgkJq33H/99deXL1++c+dOzVO2traPPvro7t27+/TpI2PC5klKSpo8ebKtra3+HfU1JElycXE5e/ZszX3sAQAAAJgC2nigaX766afnn3/+7t27tRdaWFjY2NjExsYGBQXJFazZ8vLy9u7dm5SUlJGRYWlpWVJSUlVVJYRQKBSTJ0/evHmz3AEBAAAA/AdtPNA0ERERH3zwwb179+o/pVQqR44cmZiY+Jvf/Kbtg7XcvXv3Dh48uHv37pSUlMLCQiFEWVnZl19+6evrK3c0AAAAAP/HaG18eHh4RESEUYaC6dBqteHh4XKneDhOP6D9MZe/PwAAAG3M0ohjme//culjm2n41mNeE9I2p19VVdUHH3xgY2PTpUsXe3t7Ozs7Ozs7BwcHpVKp/1mlUrV2hjZWVlZWVFTUvXt3uYPwe9qxcKABAAAexJhtPNDuderUKSwsTO4UbcrGxsbGxkbuFAAAAAD+j4XcAQAAAAAAgKFo4wEAAAAAMBu08QAAAAAAmA2zb+PHjx8vSdKhQ4faeL+fffaZSqWSJKlPnz6XL1821rAxMTFKpVKSpN69ex89etRYwwJoVfv27XvxxRdVKpWlpaWDg0O/fv327t0rdygAAAC0T2bfxu/evVutVrf9ft94443NmzcLIS5cuPDII4+0cDStVnv16lUhREhISFRUlFqtvnjx4jPPPGOEoGjXas4cGUdAYmKir6/vSy+99K9//au0tPTcuXPLli3Lzs427l6McqQ43AAAAO2A2bfxepIkyR2hRZKSkuSOALPU8jOHc6+FysrKQkJCQkNDFy9e/Nhjj3Xu3FmtVk+bNu3tt9827o6McqQ43AAAAO2AnG18SEiIJEmjR49WKpW9evVKTk7WL589e7ZKpbKxsZk2bVp1dfX8+fMlSdqzZ4+fn9/y5csPHz78zDPP2NraOjg4rFixQr9JfHy8h4eHnZ2dLF8GFhoaKknSnDlzPDw8lEplZGTkg6rz9/eXJOnChQu5ublubm5KpVIIMWnSpKysLFdX13nz5jWylzrTMm7cOEmS3NzccnNzv/jiC/37eIUQKSkp7u7uDg4OM2bMqKioqDN7bTIf+I/6p2t6evrgwYOVSqWnp6f+fdcNnj8NblvnHKh95tQ57g8as5ERRL2TR54pMzdHjhy5cePG1KlTG3zWwMPdpGMt6h0pDjcAAEDHojMSrVar1WqbupWdnd2+fftKS0vj4uIUCsW1a9d0Ot38+fOvXbt2/vx5KyurX375RafTqdXqxMTEoqKid999V6VSrV69uqSk5PLly3/4wx/0zx48eLC6uvrvf/+7QqGorq5um/A7d+6smcCaDFu2bLG1tW2kOiHE+fPndTrdiRMn7OzsdDpdZWWlEOLKlSv6rdatW6dWq+vvrs60lJSUODg47Nq1S/9scHDwtWvXrl27ZmNjk5qaevPmzWHDhq1du1ZXa/aioqLaYFpkYZpRb9y4Ued0LSgosLOz27p16507d+Li4uzs7PLy8nQNnT/1t9XVOwdqzpwHHff65+SDRtDpdA0OYlJM8yj/9a9/FUKUl5fXf8rAw92kY617wJHicAMAAHQc8r+pvnv37jY2NsHBwU5OThkZGUKItWvXajSaPn36ODo6FhcX61dzc3NzcHAYPny4ra1tSEiIra2tq6vrn/70p5pxJEl6/vnny8vL7927J0sh+gxeXl6lpaX379/XL6lfXbPVmRZbW9uAgIBt27YJISorKysrKzUaTUZGhqurq4+Pj6Oj4/jx4w8cOKDfVj97oaGhLasPTfPtt9/WOV3T0tLUavWUKVO6dOmiPyv2799fs37t86f+tuIBvxpCiAcdd1HvnHzQCI0PgkbodDrxgM/1GHi49+7da/ixFhxuAACADs9S7gD/0a1bt5s3bxYXF7/11lvp6el37tzRXzuqLTc3t2fPnrLEayF9dc3evMFpmTlz5gsvvFBcXHzgwIHXXntNCFFQUHDu3LmajsLb27vlydFs9U/XvLy8rl271jxUq9V5eXkGbtvIr4aBx73xXy5OnuZxc3MTQly4cEH/qZbaDDzcTTrWgsMNAADQ4cl/NV5Pp9Pl5OT07NkzISEhMzPzp59+Kisrq38LerVafe3aNVkStkRNdc0eocFpGT58uLu7+86dOw8fPjxmzBghhEqlGjhwYM17LdLS0oxTAJql/umq0WgKCgpqHl6/fl2j0Ri4bSO/GgYe98Z/uTh5mmfEiBFdu3Zdt25d7YVVVVXvvfeegYe7ScdacLgBAAA6PPnb+Lt375aXl69bt66iomLUqFH37t2ztrZWKpVZWVnl5eV1Vn7xxRcLCwsjIyNv3LhRWVlp+i19neqEEEql8vDhw5WVlTk5Ofp1LCwsLCwsMjMzS0tL62yu0+mKioqCg4MfNC0zZ87cvHmzo6Njp06dhBAjR47Mysratm1bSUlJaWlpUVFRWxWKBtQ/Xb29vfPz87du3VpcXBwXF1dYWPigq6D1t61/DtScOU899ZQhx72REUpLSzl5mkehUGzYsOHTTz9dvnx5dnZ2ZWXlpUuXIiMjKysrDTzcTTrWhh8pDjcAAEC7ZawP2Tf7FneOjo5WVlaenp4ZGRk6ne7XX3994okn7OzsAgICevfu3bt373feeUcI0b1798OHD+t0un/84x/6T8j37Nnzo48+mj9/vhDi0UcfLSoqGjZsmBDCz8+vDcInJCSoVCohxOOPPx4QEFCToX///kKIqVOnNlidTqd77733FAqFu7t7cHCwEOLNN9/U6XT+/v7W1tYBAQEfffSR/vb1tU2fPr3+tFRVVel0ulu3btna2mZnZ9cE+9vf/ubu7m5tbf3000//+OOP+vmpmb3Wnha5mGzUOqerTqfbt2/fwIEDbW1tPT0909LSdDrdkiVLGjx/6mzb4DlQc+bUOe4Njtn4CLp6J4+8U1efyR5lnU534MCBMWPG/OY3v7GwsFCpVM8991xqaqrO4MPdpGOtq3ekONwAAAAdiqTT6YzyckB4eHjNv4ZTKpXHjh2r/5nSNta88A/VBtXpdLqFCxeuWbOmNQZvpWlpDWYUFc3GUe5QONwAAAAPIv+b6qurq+WO0Ipar7qDBw+WlJSEh4dPmDChlXYBAAAAADA1crbx06ZNKykpGTNmzPHjx2WM0Upau7rY2FiNRiNJkv4j9wAAAACAjkDOL5xLTExMTEyUMUCrau3qtm/f3nqDAwAAAABMk/xvqgcAAAAAAAaijQcAAAAAwGzQxgMAAAAAYDaM+dn4iIiIiIgIIw7Yxsw6fCvRarVyRzCUuZ9+MBBHueMwo78/AAAAbcmYbbxWq22v3/HbMb/B2LzqbcenHzrmL2AHx+EGAAB4EN5UDwAAAACA2aCNBwAAAADAbNDGAwAAAABgNmjjAQAAAAAwG6bVxo8dO3bDhg1ypwDkwfkPAAAA4KFMq43fs2fPO++80/JxtFrt1atXWz6OXJqR39xLfqjbt2/LHaHVcf5Dj78AAAAAaIRptfHGkpSUJHeEFmlGfnMv+aFefPHFwYMHx8bG3rhxQ+4spq7dnwztHn8BAAAA0AgTauPj4+MVCkVYWJgQIjQ0VJKkOXPmeHh4KJXKyMhIIURISIgkSaNHj1Yqlb169UpOTvb395ck6cKFC7m5uW5ubkqlUggxadKkrKwsV1fXefPmCSF8fX0XLVokb2l66enpgwcPViqVnp6ee/fuFUI8NH/9khvcymRLNqLS0tKff/753XffdXFxefbZZz///PPi4mK5QxlTuz//0dS/AJIkGfLrLzjoAAAAHY3OSLRarVarbeEgM2fOXLFihf5ntVp98ODB6urqLVu22Nra6hfa2dnt27evtLQ0Li5OoVBcu3ZNCHH+/HmdTnfixAk7OzudTldZWSmEuHLlSgvD1Nby6goKCuzs7LZu3Xrnzp24uDg7O7u8vDydTvfQ/PVLrr9Va5SsM9IxNZYBAwbUPm/t7e2tra1/97vfffbZZyUlJSYVtdlM9vyXXTs4vs37C2DIr7+Ogw4AANDBWLblSwbNIEmSl5dXaWnp/fv3LS0thRDdu3e3sbEJDg5euXJlRkaG3AENlZaWplarp0yZIoQIDg5+//339+/fP3nyZEO2rVOygVsZxY8//hgdHd1mu2tEnWvvd+7cEUL84x//OHbs2Jw5c3r37j1kyJCqqqpOnTrJFLBVtJvzH83+CyDjrz8AAABMk6m38Y3o1q3bzZs35U5hqLy8vK5du9Y8VKvVeXl5TR2k7Uu+d+9eYWFhW+7xQaqrq+WOYFrM6/xHy/8CcMQBAACgZ65tvE6ny8nJ6dmzp9xBDKXRaAoKCmoeXr9+XaPRNGkEWUp+7rnnwsPD23KPD/L1119fuXKl5qG9vf29e/eeffbZ6dOn+/n5ffjhh0KIdnYpvhFmd/6jhX8BOOIAAACoYUK3uDPQ3bt3y8vL161bV1FRMWrUKKVSefjw4crKypycHP0KFhYWFhYWmZmZpaWl8katzdvbOz8/f+vWrcXFxXFxcYWFhd7e3kIIQ/LXKbn+VqZZcmuwtbW1trb+7W9/u2HDhoKCgv379wcFBdna2sqdq+2Y6fmPZv8FeOivf/1NAAAA0L6ZUBu/dOnSxMTEjz766MMPPwwNDc3LywsMDLx9+7aPj48QYvr06frVxo0bZ29vHx8fv2vXLnt7+0WLFr399tsDBgxITU0tKSmZMWOGhYWFn5+fr6/vrFmzhBA+Pj4LFy6UsS49Z2fnlJSU6OhojUYTFxe3c+dOJycnIcRD84t6JdffatasWSZYsnHZ2tp6enr+6U9/unr16vfffx8YGNilSxe5QxlT+z7/0ey/AA/99eegAwAAdDSSTqczykD6t1639huwlUrlsWPH+vXr16p7qa9tqmuQXCULWauu7/bt2w4ODg961qSith4ZTwZ5dZDjW1+HPeKiAx90AACAhzKhq/EG6oC3OuuAJdfXSA/foXAydDQccQAAANRhTm38tGnTSkpKxowZc/z4cbmztJEOWDIehJOho+GIAwAAoEHm1MYnJibqdLqrV68OHTpU7ixtpAOWjAfhZOhoOOIAAABokDm18QAAAAAAdHC08QAAGEd4eLgEtA7u+AgAqGEpdwAAANoPrVZLu9U8fD1BI5gWAEBtxmzjIyIiIiIijDigqWnf1TVIq9XKHcFQ7f70A8e3ozGjvz8AAABtyZhtfAe5BNFxLheYV40d5PQDHqSd/WlqN4UAAAAYHZ+NBwAAAADAbNDGAwAAAABgNmjjAQBoPxYvXty5c+ewsDD9w7Fjx27YsKEZ4zR7w/oyMzNHjRr1zTffNHuEmJgYpVIpSVJMTIx+yaFDh1xcXDp37vzmm28aJWQdxprGFm4LAECDuFM9AADtx+rVq2/evFnzcM+ePU3aXKvVvvXWWy4uLk3d8EG2b99+8uTJn3/+uSWDhISEdO7cefXq1atWrZo+fbqjo+OIESOOHj0aHh7+17/+1Sg56zDWNDZjWwAAHqqNrsbrX0SXJOns2bP6JZWVlVFRUfPnz2/j19dFvcsCO3bsqPlPbM1X/k6fPr15g3ecSs2IuRyUxpG5I2c2yi+svrTs7Oy2v7Cpt2DBgqVLl4qmHA60vaSkJOMOGBAQ8MEHH9jY2LR8qMjISIVCsXLlypYP1dqMPo0AANTWdm+qT01Nzc/P79u3rxCiqqrK39//97///bp166Kiotzc3FatWnXr1i0hhP719aCgoM2bN7dGjO3btyckJNS+LODn53fo0KH4+HghRFhYWHZ29ty5c1uyi45TqRkxi4PSCDJ38Mwt/4WtKa1Xr14hISFtXJ0Q4p///GdiYqL+ZwMPRzsTGhoqSdKcOXM8PDyUSmVkZKR+eXp6+uDBg5VKpaen5969e+fPny9J0p49e/z8/JYvX75o0SJJklxdXe3s7KysrNzd3Xv37m1nZ+fg4FAzn7Nnz1apVDY2NtOmTauurq690/j4eIVCoX9n+JYtW6Ratm7dWn/bSZMmZWVlubq62tra1mxYP2Qj5bQqlUr1ySefxMbGXrx4sc5T9RPWnkl9yW0/jfPmzau9rYlMIwCgHWi7Nn7AgAFdA1fSLQAAIABJREFUu3bV//z+++9369btySef1D9sy9fXG7wsEBYWFh4efubMGUtLSzc3N1dX15bsouNUakbM4qA0siGZm6T9ZW75L2yd0kTbVnf//v34+PixY8fWLDHkcLQz0dHRarV66tSpZ86c2bBhQ3R0tBDixo0br7zyypIlS65duzZ37tyJEye+9957arX65s2bn376qYODw0cffdSzZ8/Vq1cXFRV9/vnn//u//7tv377CwsL58+evX79eP7JCocjMzDx16lRSUlJmZmbtnc6YMSMwMFD/s4WFxdmzZ3U63ccff+zu7v7aa6/V31bflF65cqW0tLRmw/oh8/PzGyynDUyYMOHVV1/Vv62jRoMJ161bVzOTUVFRskzj+vXra7Y1qWkEAJg7GW5xV1VVFRcXFxQUVLPE8NfXH/S6dUpKiru7u4ODw4wZMyoqKpoaycbG5pVXXtm0aVNL6qqvJZWKB7xIb5qVmhFzPChkJnNTN6yjfmniwdUZfsHQ8OrWrl0bHBwsSZLRSzNHkiR5eXmVlpbev38/LS1NrVZPmTKlS5cuwcHBTk5O+/fvF0K4ubk5ODiEhobqN+nWrZuVlZWXl1dlZWXPnj07d+78zDPP3L59W//s2rVrNRpNnz59HB0di4uLH7TfoKCgJ5544vz58++9996WLVv0rxkZsu2DQtYvx1hT1Lh169Z99913R44cMSRh7ZlkGgEA7YYMbfyPP/6Ym5s7aNCg2gsNfH198eLF9V+3vn79emBgYExMTHZ29smTJzdu3NiMVEOGDNm9e3dL6qqvJZU2+CK9yVZqRszxoJCZzM3YsLYGSxMNVWf4BUPDq8vOzr5x48bgwYObWtqtW7fOnDlTXl7e5ILNR15eXs0bhYQQarU6Ly+vSSMUFxdPnjzZ2dm5c+fOD922urp6+vTpc+fO/e1vf2v4ti0PaVxdu3Zdu3bt4sWLa5YwjQCAjkaGNj47O9va2rpLly51ljfp9fXar1tnZGS4urr6+Pg4OjqOHz/+wIEDzUjl7Oz866+/6nS6ZtdVn1EqFbWKTU9PN81KzYg5HhQyk7mFv7APKk3Uq87wC4aG/+GNjIys8zqIgaWdO3du4sSJv/nNb1xcXLy8vGbMmLFq1art27cfO3as9i3EzZpGoykoKKh5eP36dY1G06QREhISMjMzf/rpp7KyMrVa3fjKq1evvn37dkRERJO2bXlIo5s8ebJarU5OTtY/ZBoBAB2NDG18WVmZtbV1/eXNfn29oKDg3Llz+pvNaLXaoqKiZqSytraurq427mWfjlOpGTHHg0JmMrfwF/ZBpYl61Rl+wdDA6rZu3friiy/a29vXf+qhpT3zzDOZmZnl5eWnTp2KiYkZO3aslZVVRkbGihUrBg4caGNj07t379GjRy9YsGDTpk3p6emXLl2qqqp60GimydvbOz8/f+vWrcXFxXFxcYWFhd7e3k0a4d69e9bW1kqlMisrq/HzJDMzMzIy8rPPPrO2tr5z5857771Xf1sLCwsLC4vMzMzS0lIjhmwNsbGxNV/GzjQCADoaGb433tbW9kGfopw8eXJSUlJTX19XqVQDBw48efJkS1JVVFRYWFgoFIqWDFJHx6nUjJjjQSEzmVv4C9tIaeK/qzP8gqGB1X3++efffPPNlClTapZkZGQcPXpUNKU0lUo1bNiwYcOG1V5YWFh46d/+9a9/JScnX7p0KTc3t0ePHo/9N3d39wbfidA85eXl1tbWtT/nb6DQ0NC8vLzAwMCff/7Zx8dHCDF9+vTPP/88JSVl8eLFs2fPfvzxx3fu3BkREZGXl+fv779jx45nn3120aJFOTk5gYGBe/funThxohDiqaee2rZt26xZs/Lz85ctWzZnzpxNmza5uLiMHz/e2dk5MDBwwoQJ27Zts7KyUqlUBQUFiYmJlpaW9vb258+fv3v37vDhw/V5fH19169fX2fbc+fO+fn5+fr63rt3r3PnzvoNlyxZUiekk5PTg8qpX3hISMiOHTtycnJef/31gQMH7tixo0ePHk2dvY8//vi9997r3Lnzhx9+OHPmTCFE9+7dw8LCDh06JIRwdnaun/D/s3encU2c69/A70EQCBFkDSgoYEuLCrhbFZdarKCg1gKKrC6Fal2xR6jSE0BtwVatUhRsXQCxRVq0oqUKiHWpy7GuVcSNowKyKVogIAHmeZH/k0NJwECWyfL7vvCTTGa5rov7nvHOTGaWL18urGRmZiYjZZw9e3a/fv2Ey0pTRgAAgLYYGMbb2to2NjbW1dWx2WzRT3fs2DF+/PhJkyYRQtzc3D766KP09PQZM2akp6d39NX1pEmTFi5ceODAgZkzZ1IU1dTU1Lt3765GVVVV1b9//278z6wT8sg0LCxMCTNVIar4R0HMiFnKDtt5aqRNdhKmRiTe8bZ9PnxAQIC1tXVcXJxMUhM7tm9qaiopKREO7wVj+zt37vTs2dNehJ2dXTe2npOTExkZuWnTphkzZnRp8fj4eOF9yP/66y/h9ClTprT9NsTNzW379u3Ct1u3bt26davg9Z07d4TTy8vLha/bThfYvHmz8LWw4ISQ7777rt2cosuKfeB5uyA7SUfUli1btmzZ0skMkli5cuXKlSvbTQwJCQkJCekowu3btwsrOXbsWAbL2HbZbpcRAADgH2gZ4XK5XC63o08NDAyKi4sFr5ubm/v06XPhwgWaprdu3cpms01MTL7//nvhzHv37l24cKHg9YkTJ5ycnFgslouLS25u7po1awgh/fv3f/HixaBBgwgh/v7+NE3/+OOPDg4Ourq6o0ePvnz5clVVlbm5eUJCgmgkq1atEjy0ic1mjxkzprS0VDh9xYoVgtdffvllcHAws5nSNC02WeXJVKmoxx+lowURM2IWvO52h22b2muzkzA1uis7XgF/f/+IiIi2KQtT61I63fD8+fPLly8fPHgwLi4uNDTUzc3N3t5eT0/P3t7ezc0tNDT0m2++yc3NffDgAZ/P73xVX3/9tba2toGBwVtvvXXs2DG5hq1anjx5Iva/GU+ePJFwDZpcvddCcQAAoC2KltGtzqKjo4X/imKz2RkZGaNHjzYzMyOEbNy48fnz522/55YtPp/v7+/v4uKybt06SeZvbm52dHQ8cuSIg4NDSUnJpk2b6uvr9+3bJ3ZmZKqE1OOP8sYbb3S0IGIWpVExS99h5Z0a6WJ2wtQcHR3FzqCA/U/by/KFXntZfkhISEpKiuA1m822sbGJj4/38vJSWNhqDNXrBIoDAABtKe4Wd56enubm5oILzyIjIx8+fHjx4kU5bSspKcnMzCw8PFzC+aOiotauXevo6Lh+/XpbW9sdO3ZIs3XNyVSFqMQfpZMFEbMojYpZ+g4r79RIF7MTpia/eF5LcFm+j49PREREcnKy4Gz8ixcvjh07tnLlysGDBz99+jQ1NTUwMNDCwqJPnz7jx48PCQk5d+6ccA11dXWFhYWCLy/y8/MZzAUAAAA0ioLOxovi8/lfffWVn5+fnZ2dTALotoyMDENDQw8PDwnnR6ZKSBP+KIhZGuods8Bre4FqpaZU+x+apktLSx88ePDgwYPVq1eLvS0/i8UaPHjw4MGDbWxslCRslaNUf3Rlg+IAAEBbDNziTkBHR2ft2rVMbb2tOXPmyHX9mpOpClHFPwpiloZ6xywhNU5N3iiKsra2tra2HjduXFhYmOgMWlpaPXr0uHTp0o0bNyZNmkTTtMbeRhQAAAAUgLFhPAAAgGp5/Pixjo5Oc3MzIURPT09LS6ulpcXR0fHdd98dM2bMiBEjBD+bxxgeAAAA5ArDeAAAAIk8fPiQpmknJ6cJEyaMHTt2xIgRb775ZrtBe0xMTExMDFMRqgFUryNcLpfpEAAAQFlgGA/QBX///XdSUlLv3r3ZbDaLxWKz2b179zYwMGCxWIaGhoaGhj169GA6RgCQl3HjxtXV1XXezblcLn7A3An8xrt7UDEAAGhLlsN4jToFoSGZqtB3/xrV/AA6ok69QAn3P/r6+kyHAAAAACC7YXx0dDS+KgamKLL5BQQEHDhwQOwjHlgs1ty5c7/99lv8Xx8AAAAAAOREcc+NB1AP3t7evXr1ajdRR0fH0NDwwIEDu3fvxhgeAAAAAADkB7+NB5BIS0vL+fPnjx07duTIkYaGhrYfsVisQYMGHTp0qG/fvkyFBwAAAAAAGgJn4wE6U19fn52dHRYWZm1tHRYWRtP0tm3bRowYIfiUoih9ff2VK1eeP38eY3gAUBgPD4/ExESmowAAAABm4Gw8gBgPHz7Mzs4+evTopUuXRo0a5enpGRUVZWNjI/i0sLDw+vXrra2tvXv3/uWXX0aNGsVstACgaXJycmSyHi6X+9FHH1lbW8tkbaqoGxVA0QAAgHE4Gw/wf1paWs6ePRsZGTlw4MAxY8b8+eefoaGhpaWlubm5K1asEI7hCSGenp48Hs/T0/Pu3bsYwwOA6srIyGA6BIZ1owIoGgAAMA7DeNB01dXVmZmZQUFB5ubmK1eu1NPTS0tLKy8vT01N9fHxYbPZoovY2dnl5ORkZmaK3usOAEDe9uzZo6enFxUVFRERQVHU4sWLHR0d2Wx2bGwsISQ8PJyiqClTprDZbDs7u8zMTEKIr68vRVH3798vKyuztbUV7NnmzJlTVFRkY2OzdOlSLy+vVatWMZyY7OTl5Q0ZMoTNZru4uBw/fpxIUAEJ69Z2EUKImtUNAABUBSX2uVkAau/WrVtHjx7Ny8u7dOnShAkTvLy8PD09+/Tpw3RcAKDCBE++VMDzLxctWmRpablhwwZLS8uffvpp3LhxqampS5Ysqa+vJ4Sw2exDhw65urqmpqauXLmyuLjY0tKSoqh79+698cYb165dc3V1raura25u1tHRefLkiSKvD1dAiaqrq21tbXft2uXl5XXgwIHVq1c/fPjQwsLitRWQpG4vXrxQfNGIApsWAACoBJyNBw3S0NCQl5e3YsWKfv36zZgx4+HDh8uXL6+srMzOzg4NDcUYHgBUFEVREydO5PF4zc3NgilWVlb6+vphYWGmpqanTp1iNDpFy83N5XA48+bN69Wrl6ACJ0+elHBZTa4bAACoENziDtRfZWXlb7/9dvTo0dzc3EGDBnl5eR06dGj48OFMxwUAIHcWFhbPnj1jOgqFqqioMDc3F77lcDgVFRVdXYkG1g0AAFQIhvGgnlpbW69evZqXl5ednV1UVPTuu+96enru2rWrd+/eTIcGAKAgNE2XlpZq2uMwLS0tq6qqhG/Ly8stLS27tAbNrBsAAKgQXFQPaoXH4wke825jY+Pr61tWVhYdHV1WVnbw4MGgoCCM4QFAQ9TV1TU2NiYkJDQ1NU2ePJkQwmazz507x+fzS0tLBfNoaWlpaWkVFhbyeDxGg5UxNze3ysrK9PT02trapKSkmpoaNzc3IlkFXls3dS0aAACoFgzjQR0UFxcL7mZkZWW1fft2e3v7goKCBw8ebNu2zc3NTUdHh+kAAQBkJjIyMi0tbevWrRRFVVRUBAQEvHz50tPTkxASEhIimGf69OmGhoZ79uw5fPiwoaEhIWTVqlUff/zx4MGDs7Oz6+vrFyxYoKWl5e3t7eXltWjRIk9Pz5UrVzKYlAyZmZllZWXFx8dbWlomJSUdOnTI1NSUvK4CgmVfW7dFixa1XUSd6gYAACoEd6oHVdXS0nL+/HnB3eYfP37s7u7u5eU1depUwX+8AAAUT0luJ85msy9dujRw4EBmwxBLSUokFuoGAACqAr+NBxXz/Pnz/Pz87Ozs7OzsPn36eHl5ffPNN+PGjaMoiunQAACURWtrK9MhqCTUDQAAVAIuqgfV8PDhw23btk2ZMsXOzi41NdXV1fWvv/66detWXFycq6srxvAAAAKBgYH19fXu7u5XrlxhOhZVgroBAIAKwdl4UF6NjY1nz57Ny8s7fPjwq1ev3n///eXLl7///vu6urpMhwYAoKTS0tLS0tKYjkL1oG4AAKBCMIwHpVNVVZWTkyN4zPuAAQM8PT3T09PxmHcAAAAAAAAiw2F8dHR0TEyMrNYGSoLL5Srshjq3bt06evRodnb2nTt3Jk+e7ObmlpCQwOFwFLN1AAAAAAAAlSDLs/GKHPJJCXd8lYQC6sPj8f7444/s7Oyff/5ZV1fX09MzOjp64sSJeEQcAAAAAACAWLioHhjw3//+98SJE9nZ2QUFBUOGDPHy8srLy3v77beZjgsAQFoxMTG4Nu21UKJu4HK5TIcAAADKAsN4UJCWlpZr165lZ2cfPXr08ePHkyZN8vHx2b9/v5GREdOhAQDIjApdmMYsXBbXJSgUAAC0hWE8yFdNTU1eXl5eXt6RI0dMTEwEj3kfO3aslhYedggAAAAAANBlGMaDXDx8+FBw4v3SpUujRo3y9PSMioqysbFhOi4AAAAAAADVphRnRGmaXrhwIZvNDgkJYTqW10hJSTE2NqYo6o033nj8+LGsVrtlyxY2m01R1IABAy5cuCCr1SpYc3Pz2bNnIyMjHR0dx44d++eff4aGhpaWlubm5q5YsQJjeAAAAAAAAOkpxdn4nJyc69ev19TUKP/9yYODg42MjD744IP79+9LvzYul/vRRx9ZW1uHh4f37Nlzw4YNDx48kH61ClZdXV1QUCA4925vb+/p6bl///5hw4ZRFMV0aAAAAAAAAOpGKc7GFxYW2traKv8YXuYyMjKYDqH7bt26FR8f7+rqam9vn5qa6urqeuvWrcuXL0dHRw8fPhxjeAAA6fH5/Li4uGXLlgmu2NqyZYtg+tmzZ62trXv27Dl//ny5BrBixYrIyEhCyE8//ZSTkyPXbUlJUKvi4mLhBW4KLpcK1QoAAFQd88P43bt3c7ncn3/+maKoffv2hYaGGhsb6+vrBwYGtra2EkKWLVtGUVROTo63t/fatWuzsrIcHByMjIwWLFjQ1NTEbPAREREURS1evNjR0ZHNZsfGxhJCwsPDKYqaMmUKm822s7PLzMz09fWlKOr+/ftlZWW2trZsNpsQMmfOnKKiIhsbm6VLl3ayiXYFmT59OkVRtra2ZWVlP//8s5GR0cCBAwkh7crSrmiyyrehoSEvL2/FihX9+vWbMWPGw4cPIyIiqqurs7OzQ0NDraysZLUhAABoaWnx9fV97733EhIS4uLibG1tN2zY8Pz5c0KIq6vrhQsXgoKC9u7dK78A/vOf/6SlpQlee3t7nz17ds+ePfLbnDSEtbKzswsPD1d8uVSoVgAAoAaYH8YvXLgwKirqww8/pGk6JCRET0+vsLDw5s2bGRkZhYWFhJCEhAQOh/Ps2bPdu3c3NDQEBARs2bKluLj4xo0bycnJzAYfHx/P4XD8/f1v376dmJgYHx9PCNmyZYuBgcGaNWuqqqoiIyODgoK2b98umL9Pnz6HDx8WvE5PTyeEPHny5Ntvv+1kE+0KkpmZaWRktG3btj59+nz44Yd+fn4nT54sLy9vV5a2RZP+iW4VFRWpqam+vr5WVlbR0dF9+vT57bffHjx4kJyc7OXl1bNnTynXDwAAor744gsLC4uRI0cK3sbGxurp6a1fv14xW29ubt6zZ4+Hh4dwSlRUVHR09O3btxUTQJe0qxVRbLlUq1YAAKAGmB/Gt7N9+3ZLS8s33njDxMSktrZWON3W1tbIyGj06NE2Njaenp4mJiYzZsw4ffo0g6G2RVHUxIkTeTxec3OzYIqVlZW+vn5YWJipqempU6e6veZ2BWGxWH5+fgcOHCCE8Pl8Pp9vaWl56tQpsWURFC0iIqLbWy8oKHBxcXFycsrPz/fx8Xn06NHZs2cjIiIElwAAAICctLS0JCUlBQUFCacYGxvv3Llzx44dondRycvLGzJkCJvNdnFxOX78OOngYrEuXc62ffv2sLCwtr+Q0tfXnzVr1q5du2SWpIyI1op0XC4Ja0W6Ui4VqhUAAKgH5RrG19bWzp0718zMrGfPnhUVFaIzVFVV3b17l6IoiqK4XO6LFy8UH2RXWVhYPHv2rHvLii3IwoULs7Oza2trT5w4MXv2bCLPshgaGiYlJZWXl6ekpPj4+Eh/Yh8AACRx+fLlsrIyZ2fnthNnzpz5wQcfCH6ALVRdXT1r1qw1a9Y8ffp0yZIlH374YWVlpejFYqLXbXWy9eLi4urq6iFDhrSbPnTo0CNHjsgqR1kRWysirlwS1ooQInm5VKtWAACgHpRrGJ+amlpYWHjt2rWGhgYOhyM6g7GxsZOTE/3/5ebmKj7ILqFpurS0tG/fvt1bXGxBRowY4eDgcOjQoXPnzrm7uxN5lmX48OFjxozR0lKudgIAoPaKi4t1dXV79erVbnpCQsLvv/9+/vx54ZTc3FwOhzNv3rxevXoJLgE7efKk8FPhxWJ5eXmSX84WGxvb7ssCATMzs0ePHtE0LV1yMtZRrYhIuSSsVXNzc0eXuYlSrVoBAIB6UK7h2atXr3R1ddlsdlFRUWNjo+gMkyZNKioqOnDgQH19PY/HU+az8XV1dY2NjQkJCU1NTZMnT2az2efOnePz+aWlpYIZtLS0tLS0CgsLeTxeu2Vpmn7x4kVYWFhHBVm4cOHevXtNTEx69OhBVKosAAAgiYaGBl1dXdHp5ubm27dvX716tXBKRUWFubm58C2Hw5Hycrb09PT333/f0NBQ9CNdXd3W1laxB2gGdVQrIlIuCWtFJC6XytUKAADUA/PD+Nzc3Pj4+F9++eXdd9+dPn3633//bW1tvWHDBjMzs4CAgNbW1uXLl1dUVPj6+v7xxx/W1tapqakxMTGmpqaTJ09W/FPW09LSFixYQAhxcHB48uRJRERERUVFQEDAy5cvPT09CSEhISGCOadPn25oaLhnz57Dhw8bGhquWrXq448/Hjx4cHZ2dn19/YIFC7S0tLy9vb28vBYtWvTNN9989tlnFRUVgv8xaGlpGRsbNzU1eXt7ixaEEBIQEHDp0iVvb2/BtkTL0rZoCi4RAABIj8VidfR77Llz53I4nMzMTMFbS0vLqqoq4afl5eWWlpaiS0l+3db+/fvnzZsnOB6lp6fHx8e/8847go+ampq0tLT09PS6n5gcdFIr8s9ySVgrInG5VK5WAACgHrSZDoBMmTKlpqZG+PbOnTvtZti+fbvwTu+EkDlz5syZM0dBwYkIDAwMDAwUvo2Pjxf8iI4Q8tdff7Wd88yZM23vAxcbGyu8a05SUpLgRdvnxq9cuVLsFkULQgjp3bv3okWLbG1thVPalWX48OFtiwYAAKrF1ta2sbGxrq5O8IzSdnbs2DF+/PhJkyYRQtzc3D766KP09PQZM2akp6fX1NS4ubmJLjJp0qSwsLADBw7MnDmToqimpqbevXuL3XTbZ54HBARYW1vHxcUJ3lZVVfXv37/tvdyUQee1Im3KJWGtCCGTJk1auHDha8ulcrUCAAD1wPzZeHUlOG0uc2fOnKmvr4+Ojp45c6Y81g8AAMpgxIgRffr0uXXrFiFEcMVWcHDw7t27BZ9aWVlFRUUJXpuZmWVlZcXHx1taWiYlJR06dMjU1FT0YrGoqKh2121VV1dbWFh0/tDTdm7evDljxgxZ5yqttrUinZZLwlqFhISIXubW1XIpZ60AAEA9ULK6+Up0dLTwX+Un12gDAwP379/ft2/fI0eODBs2TLYr9/PzO3r06OrVqxVQatX6mwIAME62u82NGzc+f/588+bNMlmbKD6f7+/v7+Lism7dOknmb25udnR0PHLkiKOjo/RbV61akS6WS7a1IjgiAwDAP+FsvOylpaXRNF1SUiLzMTwh5IcffqitrcWBHABA7UVGRj58+PDixYtyWn9SUpKZmVl4eLiE80dFRa1du1ZW41LZknetSBfLpcy1AgAANcD8b+MBAABAVI8ePQ4ePPjVV19ZWFjY2dnJfP3Lli2TfOaMjIyJEyd6eHjIPAyZkHetSFfKpeS1AgAANYBhPAAAgJLS0dFZu3Yt01EQQgiDN5eVEGoFAACaAxfVAwAAAAAAAKgMWZ6Nj4mJiYmJkeEK5U21omUEl8tlOgQAAAAAAAD4H1kO47lcrrreek0z7xCrafkCAAAAAAAoP/w2HgAAQGZU7sI0ZqFWksP1cQAAIITfxgMAAMhGdHQ0DaqvtbU1OTnZzMzsm2++aW1tZTqc/4NL5AAAQAjDeAAAAID/oSgqNDT0woULP/30k7u7e0lJCdMRAQAA/AOG8QAAAADtDRgwoKCgYPLkycOGDdu1axfT4QAAAPyPcg3jPTw8EhMTmY4CAAAAgGhra0dEROTn5+/cudPb27uqqorpiAAAAAhRtmF8Tk7OJ598Iv16uFyuSl8C1434VT1lAAAA5eTk5HTp0qXBgwe7uLgcOnSI6XAAAACUbBgvKxkZGUyHIJVuxK/qKQMAACgtHR2d6OjorKysyMhIX1/fmpoapiMCAACNpkTD+D179ujp6UVFRRFCIiIiKIpavHixo6Mjm82OjY0lhISHh1MUNWXKFDabbWdnl5mZ6evrS1HU/fv3y8rKbG1t2Ww2IWTOnDlFRUU2NjZLly4lhHh5ea1atYrZ1ATy8vKGDBnCZrNdXFyOHz9OCHlt/KIpi11KaVMGAABQG++88861a9fs7e2HDRuWn5/PdDgAAKDBZPUcFC6Xy+VypVzJwoUL161bJ3jN4XDOnDnT2tq6b98+FoslmGhgYHDixAkej5eUlKSnp/f06VNCyL1792iavnr1qoGBAU3TfD6fEPLkyRMpg2lL+uyqqqoMDAzS09P//vvvpKQkAwODiooKmqZfG79oyqJLySNlWkZ/UwAAADVz4sSJfv36hYaG1tbWMh0LAABoIiU6Gy8WRVETJ07k8XjNzc2CKVZWVvr6+mFhYaampqdOnWI0ui7Izc3lcDjz5s3r1auXIPiTJ09KuKyKpgwAAKCWpkyZcuPGDUKIi4vL6dOnmQ4HAAA0jrIP4zthYWHx7NkQ6xh6AAAgAElEQVQzpqOQVEVFhbm5ufAth8OpqKjo6kpUK2UAAAB1ZWRklJycnJCQMG/evBUrVrx69YrpiAAAQIOo6jCepunS0tK+ffsyHYikLC0t2z6opry83NLSsktrULmUAQAA1Nu0adOuXLny5MmT4cOHX7lyhelwAABAU6jeML6urq6xsTEhIaGpqWny5MlsNvvcuXN8Pr+0tFQwg5aWlpaWVmFhIY/HYzbUttzc3CorK9PT02tra5OSkmpqatzc3AghksTfLmXRpZQzZQAAALVnYWGRlZXF5XKnTZsWGRkpuFsNAACAXCnRMD4yMjItLW3r1q2bNm2KiIioqKgICAh4+fKlp6cnISQkJEQw2/Tp0w0NDffs2XP48GFDQ8NVq1Z9/PHHgwcPzs7Orq+vX7BggZaWlre3t5eX16JFiwghnp6eK1euZDAvATMzs6ysrPj4eEtLy6SkpEOHDpmamhJCXhs/EUlZdKlFixYpYcoAAAAawsfH5+rVq3/99df48eOLioqYDgcAANQcRdO0TFYUHR0t/Fd+2Gz2pUuXBg4cKNetiFJMdmIxlTJhNGsAAACVQ9P0d999t27duk8//fRf//qXlpYSnSwBAAB1onoHmNbWVqZDUDQNTBkAAEDlUBQVGhp66dKlX3/9dcKECQ8ePGA6IgAAUE+qNIwPDAysr693d3fXnLvIaGDKAAAAKs3Ozq6goMDHx2fMmDG7du1iOhwAAFBDqjSMT0tLo2m6pKRk2LBhTMeiIBqYMgAAgKrT0tJasWJFQUFBcnLytGnTysrKmI4IAADUiioN4wEAAABUxaBBgy5evDhx4sShQ4fu37+f6XAAAEB9YBgPAAAAIBfa2toRERHZ2dkbN2709fV99uwZ0xEBAIA6wDAeAAAAQI5GjRp19epVe3t7Z2fn7OxspsMBAACVJ8sHzsXExMhkVaA8uFwuHjgHAAAgE+fOnQsJCRkzZkxiYmKvXr2YDgcAAFSVLM/Gc7lcWgNwuVzNyVSGzQMAAEDDjRs37s8//9TX13dxcTl16hTT4QAAgKrCRfUAAAAACmJoaJicnLxjx47AwMCwsDAej8d0RAAAoHowjAcAAABQKHd395s3b/J4vJEjR16+fJnpcAAAQMVgGA8AAACgaL17905LS4uOjvb09IyMjGxqamI6IgAAUBkYxgMAAAAww8fH5/r164WFhSNHjrx+/TrT4QAAgGpQ0DCezWZTFEVR1J07dwRT+Hx+XFzcsmXLBB9t2bJFMP3s2bPW1tY9e/acP3++nIIpLCycPHnyb7/9Jnj7008/5eTkCF5HR0cL4gwJCeneyjUnUwAAAJAeh8P55ZdfVq9ePWXKlPj4+JaWFqYjAgAAZae4s/HZ2dmVlZVvv/02IaSlpcXX1/e9995LSEiIi4uztbXdsGHD8+fPCSGurq4XLlwICgrau3evPML44YcfUlNT237h7e3tffbs2T179hBCoqKiiouLlyxZIs0mNCdTAAAAkImgoKBLly4dP358/Pjx9+7dYzocAABQaoobxg8ePNjc3Fzw+osvvrCwsBg5cqTgbWxsrJ6e3vr16xUQhp+f35dffqmvr992YlRUVHR09O3bt7W1tW1tbW1sbKTZhOZkCgAAALJia2ubn58fEhIyduzYbdu20TTNdEQAAKCkGPhtfEtLS1JSUlBQkHCKsbHxzp07d+zY8eDBg3Yz5+XlDRkyhM1mu7i4HD9+PCIigqKoxYsXOzo6stns2NhYwWxZWVkODg5GRkYLFizoxk1i9PX1Z82atWvXLmnyEiVNpoQQsckqZ6YAAAAgPYqiQkNDT58+nZ6e7u7uXlJSwnREAACgjBgYxl++fLmsrMzZ2bntxJkzZ37wwQeRkZFtJ1ZXV8+aNWvNmjVPnz5dsmTJhx9+uHr1ag6H4+/vf/v27cTExPj4eEJIeXl5QEDAli1biouLb9y4kZyc3I2ohg4deuTIEWnyEiVNppWVlfHx8e2SVdpMAQAAQFYcHR3/+OOPyZMnDxs2DN+8AwCAKAaG8cXFxbq6ur169Wo3PSEh4ffffz9//rxwSm5uLofDmTdvXq9evcLCwkxNTU+ePCn4iKKoiRMn8ni85ubmU6dO2djYeHp6mpiYzJgx4/Tp092IyszM7NGjR7K9gE0mmZI2yebl5SlnpgAAACBD2traERER+fn5O3fu9PHxqa6uZjoiAABQIgwM4xsaGnR1dUWnm5ubb9++ffXq1cIpFRUVwh+ZE0I4HE5FRYXoglVVVXfv3hXcd53L5b548aIbUenq6ra2tjY2NnZj2Y5oTqYAAAAgc05OTufPnx8wYICzs/Phw4eZDgcAAJQFA8N4FovV0Y+6586dy+FwMjMzBW8tLS2rqqqEn5aXl1taWoouZWxs7OTkRP9/ubm53YiqqalJS0tLT0+vG8t2RHMyBQAAAHnQ09OLi4v7+eef16xZ4+vrW1NTw3REAADAPAaG8ba2to2NjXV1dWI/3bFjR2JiouC1m5tbZWVlenp6bW1tUlJSTU2Nm5ub6CKTJk0qKio6cOBAfX09j8fr3jnqqqqq/v37UxTVjWU7ojmZAgAAgPyMGTPm+vXrVlZWQ4cObfuzOwAA0EwMDONHjBjRp0+fW7duEUK++eabzz77LDg4ePfu3YJPraysoqKiBK/NzMyysrLi4+MtLS2TkpIOHTq0adOmioqKgICAly9fenp6EkJCQkKsra1TU1NjYmJMTU0nT5784MGD6upqCwuLb7/9VnTr4eHh/fr1Ky0t9fHxGTt2bFlZmWD6zZs3Z8yYoTyZmpqaRkREtEs2KipKOTMFAAAAudLX19+2bdt33303f/78sLCw+vp6piMCAADGULK61Vl0dLTwX1FsNjsjI2P06NFmZmaEkI0bNz5//nzz5s0y2bQoPp/v7+/v4uKybt06SeZvbm52dHQ8cuSIg4NDSUnJpk2b6uvr9+3bJ3ZmZAoAAABMefny5Zo1a/Ly8vbt2zd+/HimwwEAAAYo7my8p6enubn5nTt3CCGRkZEPHz68ePGinLaVlJRkZmYWHh4u4fxRUVFr1651dHRcv369ra3tjh07pNm65mQKAAAACmZkZJScnLxp0yZfX9/IyMhXr14xHREAACiags7Gi+Lz+V999ZWfn5+dnZ1MAui2jIwMQ0NDDw8PCedHpgAAAMC4ysrKjz/++N69e6mpqUOHDmU6HAAAUBxtpjaso6Ozdu1aprbe1pw5c+S6fs3JFAAAABTGwsIiKysrMzPT3d198eLFn3/+eY8ePZgOCgAAFIGBW9wBAAAAgEz4+Phcu3btP//5z7hx44qKipgOBwAAFAHDeAAAAAAVZmVldfTo0QULFri6usbHx7e2tjIdEQAAyBeG8QAAAACqjaKo0NDQixcv/vrrr1OnTn3y5AnTEQEAgBzJ8hZ3MTExMlkVKA8ul4tb3AEAAKiK1tbWhISEjRs3btiwITQ0lOlwAABALmQ2jAcAAAAAZXDr1q2goCAOh/P999/36dOH6XAAAEDGcFE9AAAAgFoZNGjQhQsXJk6cOHLkyJ9//pnpcAAAQMZwNh4AAABAPV28eDE4ONjZ2TkpKcnExITpcAAAQDZwNh4AAABAPY0ePfrq1av29vZOTk5Hjx5lOhwAAJANnI0HAAAAUHNnz56dP3/+5MmTN2/ezGazmQ4HAACkgrPxAAAAAGrO1dX1zz//JIQ4Ozv//vvvTIcDAABSwdl4AAAAAE2Rk5MTGho6bdq0rVu3slgspsMBAIDuwNl4AAAAAE3h4eFx8+ZNHo83cuRIwfl5AABQOT2io6OZjgEAAAAAFERPT2/27NkWFhYBAQHV1dUTJkzo0aMH00EBAEAX4Gw8AAAAgMbx8fG5du3a7du3R44ceePGDabDAQCALsDZeAAAAABNxGaz/fz8DAwMAgICWltbx4wZo6WFEzwAACoAt7gDAAAA0Gj//e9/58+f39TUlJKS8sYbbzAdDgAAvAa+cwUAAADQaLa2tidPngwODh4zZsy2bdtwjgcAQMnhbDwAAAAAEELI7du3g4ODTU1Nd+/e3bdvX6bDAQAA8XA2HgAAAAAIIWTgwIHnz59/9913hw0btmvXLqbDAQAA8XA2HgAAAAD+4caNG0FBQQ4ODjt27DAzM2M6HAAA+AecjQcAAACAf3B2dr5w4YK9vb2zs/Mvv/zCdDgAAPAPOBsPAAAAAOKdP38+ODh4yJAhycnJxsbGTIcDAACEyPBsfHR0NAVqJzo6WlYtBLqB6b8/qAymm6oawkEN1IP0x/ExY8Zcu3bNyspq2LBhJ0+elEX3AgAAaWnLcF1cLlclRn2CIFUiVGahRMoA18u0Q1G4hqg9CsN4+VCVg5rqwuFY3mRVWxaLtW3btunTp4eEhHh4eGzZssXAwEAmawYAgO7Bb+MBAAAA4DXef//9mzdvEkJcXFzOnj3LdDgAABoNw3gAAAAAeD0jI6Pk5OS4uDgfH5/IyMimpiamIwIA0FAYxgMAAACApLy9va9fv15UVDRixIirV68yHQ4AgCbCMB4AAAAAusDCwuLQoUOff/65u7t7dHR0S0sL0xEBAGgWDOO7JiUlxdjYmKKoN9544/Hjx7Ja7ZYtW9hsNkVRAwYMuHDhgqxWC0AI8fDwSExMlHIlq1ev7tmzZ1RUlOSLLF26tEePHvv375dy011y7949Ly8vU1NTPT29fv36HTx4sJOZu1EZKbsqIzUBkInHjx/b29tTFNXY2CiTvYokTpw48f777xsbG2traxsZGQ0cOPD48eMK2K5Ywu7f1m+//SaTlbctL5HRflsBfHx8/vOf/5w5c8bV1fXu3btMhwMAoEEwjO+a4ODgvXv3EkLu37/fr18/KdfG5XJLSkoIIeHh4XFxcRwO58GDB++8844MAgXNI2xO7eTk5HzyySdSrnzz5s3z5s3r0iLffvvtyJEjpdxuV82ZM6dPnz537tx59uzZ9u3by8rKiEwrI2VXZaQmwJSOGp6K6tev3x9//CF4LZO9ymulpaV5eXlNnTr1zz//5PF4d+/e/eyzz4qLi8XOrIBqC7s/TdM0TfP5/EOHDslq5W3LSxRVYZno169fXl7e/Pnzx40bFx8f39raynREAAAaAcN4JmVkZDAdAqgPmTSnCxcu3Lp1S/r1MILP51+7di0yMtLc3NzAwGDWrFkrV64kqAwwRPKGpyqtq3uPNuxedg0NDeHh4REREatXr7a3t+/ZsyeHwwkMDPz444/Fzq/4amtra8+aNcvd3V36VQkosryyRVFUaGjoxYsXjx07NnXq1CdPnjAbDwCAJlCKYXxTU5Ovr6+BgYGZmZngXHdWVpaDg4ORkdGCBQuampq4XC5FUcbGxleuXAkICNDR0UlOTmY6ahIREUFR1OLFix0dHdlsdmxsLCEkPDycoqgpU6aw2Ww7O7vMzExCiK+vL0VR9+/fLysrs7W1ZbPZhJA5c+YUFRXZ2NgsXbq0k62EhoYaGxvr6+sHBga2trZOnz6doihbW9uysrKff/5ZcJEhEanYsmXLKIrKycnx9vZeu3atQuoBCtWu17RtTm3/+paWlnp6eoKL4cW2WELIsWPH3n77bT09PRsbm88+++ytt9567dbz8vKGDBnCZrNdXFyE17iKnSiwatUqHR2db775RmxfENtrukFHR8fBwaHd4gqrjHLWBOTk3Llz77zzDovFMjIyWrduHRHZV7fbw7fbRRNxrUu0tbTbk3e0/xfVbnMdtXDRLMS22OPHjw8ZMkRPT8/JyYkQsmfPHnnsVdo5f/58dXW1v7+/2E+lr7Zosl2t9tq1a5ubmwWvxdahe+WVpMLSl1ce7O3tCwoK3Nzchg8fvmvXLqbDAQBQd7SMcLlcLpfbvWV//PHHqVOn8ni8v/7666uvvnr69Km+vn52dvazZ8+GDx++fft2mqb//e9/jx07tqmpaf/+/T///DNTodI0LbiITvCaw+GcOXOmtbV13759LBZLMNHAwODEiRM8Hi8pKUlPT+/p06c0TRNC7t27R9P01atXDQwMaJrm8/mEkCdPngiWSkhIEF6q19ayZcuePn167949HR2dv/76q76+3sjI6PDhw4JPw8LCnj59KrZiHA4nLS3txYsXcXFx3ctUykKB9Drpoe16Tbvm1Pavv3DhwnXr1gmnt2uxjY2NvXr1Sk9Pr6+vDw8Pf+eddzraYnBwsGA9VVVVBgYG6enpf//9d1JSkoGBQUVFhdiJo0ePTktLo2k6Li4uPz9fmFS7vkB30Gu6WhOapi9cuGBtbe3q6rp79+66ujpapKNJXxmxXVWZawLd08kOsLq62tjYePPmzfX19Y8fP/70009pkX1124YnuosWbV1iWwv9zxYrdv8vGl5HR4R2LVw0C7ExVFRU6Onpffvttw0NDffu3SOENDQ0yGqv0kmRv//+e0JIY2Oj2E+lrDbdQZ99bbUTEhLa/g+Kz+cLQ2pXB2nKS9N0JxWWvLydV1h+bt68OXTo0GnTppWWlip40wAAmkMpzsaz2ewrV66cOHHi7bff/vTTT0+dOmVjY+Pp6WliYjJjxozTp08TQv7973/r6OjMnj372bNns2fPZjrkf6AoauLEiTweT/jFvJWVlb6+flhYmKmp6alTp6RZ+fbt2y0tLd944w0TE5Pa2loWi+Xn53fgwAFCCJ/P5/P5lpaWYitGCLG1tTUyMoqIiJAuP1BG7XqN6Ayd/PXbttgnT57U1tbOnDmTxWJNmzbtwYMHr910bm4uh8OZN29er169BI385MmTYicK5v/888/HjRs3efLkzlcrk14zevTo+/fvL168ePfu3Q4ODleuXBGdRx6VUeaagMzl5+ezWKzw8HAWi2VjY/PVV18RkX112/lFd9GirauT1iJssWL3/6LhdXREIP9s4aJZiI0hJyeHw+F88sknenp6gktFxJJyryKKpmnS8XXmUlabdNBnJam28Fu8zz77rJM6HD9+XIblbbvm4uJi6csrV4MHD7548eLIkSOHDx+elZXFdDgAAOpJKYbx06ZNW7ly5ccff2xra5uXl1dVVXX37l3BPWC5XO6LFy8IIT169IiPjz969Oibb77JdLxdYGFh8ezZs24vXltbO3fuXDMzs549e1ZUVAgmLly4MDs7u7a29sSJE4JvNMRWDNRbu17T7fUIri3/5ZdfGhoajh071tE1um1VVFSYm5sL33I4HME5JdGJhJDExMQffvjh6NGjkockZa/R1dWdN2/euXPn3N3dw8PDu72eLlVGyWsCslVWVta3b9+2U8Tuq4VEd9Girauj1tKO6P5flIRHBNEsxMbw9OnTrt7PtRt7FVG2traEkPv374t+JH21SQfJtltV59X+4osvtLW1O4pfycsrbzo6OtHR0YcPH167dq2vr+/z58+ZjggAQN0oxTCeoqi1a9eWlJQsWrRo6dKlxsbGTk5OwgsGcnNzCSF1dXX79u07cOBASEhIeXk50yFLhKbp0tLSdgfyLklNTS0sLLx27VpDQwOHwxFMHDFihIODw6FDhwQDFUKI2IqBemvXa7q9HjabHRcX99FHHxkbG58/f37Hjh2vXcTS0rKqqkr4try83NLSUuxEQsjs2bOPHDmSmJh45swZSeKRptfU1dUJf5pLCJk5c6Y0d1rqUmWUtiYgDxwO5+nTp22niN1XC4nuokVbV0etpR3R/b8oCY8IolmIjcHY2LjtREl0Y68iytXV1dzcvN1F7C0tLZ9//rn01e4o2XarkqTaHVHy8irG6NGjr169am9v7+Tk1KUvLgEA4LWUYhj/3Xff5ebmtrS0jBw5kqKoSZMmFRUVHThwoL6+nsfjvXjxorW1NSoqKjY21s/Pz9fXNygoSHC5ndKqq6trbGxMSEhoamoSXDfLZrPPnTvH5/NLS0sF82hpaWlpaRUWFvJ4vHaL0zT94sWLsLCwV69e6erqstnsoqIiwbNkBRYuXLh3714TE5MePXoQQkQrpqhEgTHtek0nzalzPB4vIyPjr7/+amxsPH/+vCQndtzc3CorK9PT02tra5OSkmpqatzc3MROJIRYWVkNHDjwq6++CgwM/Pvvv4m4viAg2mu6ISUlJT8/v7Gx8dGjR4mJiZMmTZJrZYRdVZlrAjL3/vvv19TUxMbGVldX8/n8p0+fiu6r2zY80V20aOvqqLWIarf/FyXhEUE0C7ExvPvuu3fv3k1LS6urqzt27Jgk9enGXkWUnp5eYmLi7t27165dW1xczOfzHz58GBsby+fzpa826WA/JhpG59V+9epVSEiI2PiVvLwKo6+vHxcXl5KSsnTp0rCwsLq6OqYjAgBQF7L6kb00t1HJzs7u06ePtra2g4NDbm4uTdM//vijg4ODrq7u6NGjL1++PGbMmJ49e/74448vX74UfPU+ZcoURkJNTU01NjYmhLz55pt+fn6EkP79+7948WLQoEGEEH9/f5qmDQwMTExMdHR0XFxcTp06JVjw888/19PTc3BwCAsLI4TMnz+fpmlfX19dXV0/P7+tW7eK/iguJCTk0aNHb731loGBgZ+f34ABAwYMGNDS0kLT9PPnz1ksVnFxsTCwdhVbtmwZIcTKyurcuXOMFApkopMeKtprhM2p7V8/IiKiZ8+eLBYrPj5+zZo1oi22sbFxzJgxgiZHUZS9vb2w0bb16aef6ujosFisr7/+mqbpEydOODk5sVgsFxcXwdZFJ27atElLS8va2rqpqUnwuARbW1u6g74gttd0tSZNTU3+/v42Njba2tqWlpbz589//vy5bCvTUVdV2ppAt3W+AywoKBgxYgSLxerbt+/WrVvF7quFDY8W2UWLbV2iTUjsnlx0/y+q3ebEtnDRLOgOmvHOnTutra2NjY0DAgIEMctkr/LaItM0ffr0aXd39969e2tpaRkbG48bNy47O1sm1RZN9rXVFtv9XV1daZoWW4fulXfWrFmd7528vb0lLK8kFVaYly9fhoaG2tnZdb43AwAACSnFMF7B5B2qgYHBrVu35Ld+mqZbW1uXL18u103QKvU3VVcKGJ5VV1cHBwc3NTXRNN3c3Pzvf//7gw8+kPdGRUneaxQ2ZGW8MkpYE40i1x2gNK1LMft/aUienWKOMmpW7S6lo2zH8V9//bVv377Lly/v6BkEAAAgIaW4qF79tLa2ymnNZ86cqa+vj46Onjlzppw2ARolPz//8ePHL168aGpqElyJeujQIUpESUmJvCORX6/pnnaVOX369NChQxUcg7LVBGSle62r3f6/pKREtKsqprd2Thn6jvTxKO3RVtnK2yUeHh5Xr14tLS0dPnz4n3/+yXQ4AAAqDMN4GQsMDKyvr3d3dxf7pCvpCe6ERFEUfigLMjF9+nQOhyO4QnXq1Klz5swRnORpx9raWn4xyLvXdE+7yowbNy4yMlJhW1fOmoCsdK91tdv/W1tbi/16Xq69VRLM9h1ZxaO0R1tlK29XmZub//TTT1wud/r06ZGRkXw+n+mIAABUEkXL6F5x0dHRwn+VnAqFyiwUinEUJbMeqjZQE1GoiTxgB6gAKLK8KXOFy8vLQ0NDKyoqUlJS3n77babDAQBQMTgbDwAAAAAKZWlpeeTIkU8++WTChAnx8fH4DREAQJdgGA8AAAAADAgKCrp06VJOTs748ePv37/PdDgAACoDw3gAAAAAYIatrW1BQUFwcPD48eN37dqFnwgBAEhClr+Nj4mJkcmqQHlwuVzl/E2dhqAoiukQQDXgP74yh4MaqAcVOo7fvn07ODjY1NR09+7dffv2ZTocAAClpi3DdanQoaKrlPkmMfKjafkqJwzPCG7h9jr4ukdO1PigxiDNPJ4yRbXqPHDgwPPnz2/evHnYsGFff/11YGAg0xEBACgvXFQPAAAAAMzT1taOiIjIzc39+uuvfX19q6urmY4IAEBJYRgPAAAAAMrC2dn54sWL9vb2zs7OR44cYTocAABlhGE8AAAAACgRPT29uLi4n376afXq1UFBQbW1tUxHBACgXDCMBwAAAAClM3bs2OvXrxsbGzs7OxcUFDAdDgCAElGuYbyHh0diYiLTUQCoP/Q1AOWHfgrAYrG2bduWlJQUHBwcFhZWX1/PdEQAAEpBuYbxOTk5n3zyifTr4XK5JSUl0q+HKd2IX9VT1kw8Ho/P5zOyafS1rkKvVD8vX75kOoTXQD9lFnq98pg6deqNGzcIIS4uLufOnWM6HAAA5inXMF5WMjIymA5BKt2IX9VT1kxXr141MzMLDg7+/fffW1tbmQ6nOzSn4aFXqp8lS5YMGDAgPj7+yZMnTMciX2iK3YNer1R69+6dnJz85Zdfent7R0ZGNjU1MR0RAACTlGgYv2fPHj09vaioKEJIREQERVGLFy92dHRks9mxsbGEkPDwcIqipkyZwmaz7ezsMjMzfX19KYq6f/9+WVmZra0tm80mhMyZM6eoqMjGxmbp0qWEEC8vr1WrVjGbmkBeXt6QIUPYbLaLi8vx48cJIa+NXzRlsUspbcrwWq2trfv3758+fbqZmdny5cv//PNPBWxU2Nck7GhEgraqoq2uq72SoqhuFIegVyqlpqamhw8fcrncN99809nZeceOHUr1dCv0UznBsVil+fj4XLt27c6dOyNGjLh27RrT4QAAMIeWES6Xy+VypVzJwoUL161bJ3jN4XDOnDnT2tq6b98+FoslmGhgYHDixAkej5eUlKSnp/f06VNCyL1792iavnr1qoGBAU3TgquUnzx5ImUwbUmfXVVVlYGBQXp6+t9//52UlGRgYFBRUUHT9GvjF01ZdCl5pEzL6G8KnTh79mzv3r2FnVFbW9vAwMDMzCw8PPzOnTs0Tcuwh7Yj7GsSdjRagrYqP3KqQ/d6pbIVh5ZnO1Fv3t7ebY+GBgYGurq6Tk5OycnJL1++VIYdoGr1UwkxW1hVPBZLQxmasZwcPHjQwsKCyy31YcoAACAASURBVOU2NzczHQsAAAO05fkVgQxQFDVx4kQej9fc3KytrU0IsbKy0tfXDwsLW79+/alTp5gOUFK5ubkcDmfevHmEkLCwsC+++OLkyZNz586VZNl2KUu4lExcvnw5Pj5eYZvTNI8ePWr7trm5ubm5ub6+PjExcefOnX379iWElJaWCl7I1Ws7miJbncJ0u1cqYXHQT7uhrKys7VvBrbNu3rz56aefLl++vF+/fsOHD3/16pWuri5DAbanmf1UtlT0WAyifHx8Ro0atWDBAldX15SUFAcHB6YjAgBQKGUfxnfCwsLi2bNnTEchqYqKCnNzc+FbDodTUVHR1ZUoPuVXr17V1NQocosa5e+//6ZpmukoXkO1OlqXSN8rlac46Kfd0NzczHQIMqM8TVHJqeixGMTq379/Xl7ed999N27cuE8//fRf//qXlpYS/VYUAECuVHUYT9O0Ys5SyoqlpWVVVZXwbXl5uaWlZZfWwEjK48aNi46OVuQWNcq5c+eOHTsmfKutra2rq6uvrx8UFBQaGvrWW29RFMVsI1e5jtYlUvZKpSpOXFwc0yGoHh8fn0uXLgnfGhgYNDc3Ozg4LF26dO7cuVu2bCGEKM+p+E4oVVNUcip6LIaOUBQVGhrq5uYWHBycn5+/e/duGxsbpoMCAFAE1fvasq6urrGxMSEhoampafLkyWw2+9y5c3w+v7S0VDCDlpaWlpZWYWEhj8djNtS23NzcKisr09PTa2trk5KSampq3NzcCCGSxN8uZdGllDNlkERzc7OWlpaBgYGxsfHixYt///33qqqqzZs3v/XWW8wG9tpWR1S/4XW7V2pCcTSHrq6u4CfxmzZtKikpuXHjRmhoqKGhIdNxSQRNsatwLFZL9vb2BQUF77333vDhw3ft2sV0OAAACiGrH9lLfxuViIiInj17slis+Pj4NWvWEEL69+//4sWLQYMGEUL8/f1pmjYwMDAxMdHR0XFxcTl16hRN059//rmenp6Dg0NYWBghZP78+TRN+/r66urq+vn50TQ9ffr0FStWMJ4dTdMnTpxwcnJisVguLi65ubmCia+NXzRlsUvJPGVarW+NoyTOnj1raGgYFBR06tSplpYW0Rlk2EPbEvY1wU5Ako5Gv66tyqrViSWnOtDd6pXdKA4tu14plvzqo97mzZtnb28fFxf3+PFj0U8Z3wGqXD+VEOOFVbljsTQYr7aC3bx5c+jQodOmTSsrK2M6FgAA+aJoGf00V3DptbwvwGaz2ZcuXRo4cKBctyJKMdmJxVTKhNGsNQSPx9PR0dHR0eloBoqSWQ/tEgZbnVhM1UEsZSsOUbL6qJCXL18aGRl19Kny7wCVsClKQvkLKxaqrSr4fP7GjRt37dqVmJj4wQcfMB0OAIC8qN5F9a2trUyHoGgamLKGYLFYnYzhmYVW1wkURz10MoZXFWiKioRqqwQdHZ3o6OisrKzIyEhfX9/nz58zHREAgFyo0jA+MDCwvr7e3d39ypUrTMeiIBqYMjAOra4TKA4oCTRFRUK1Vc4777xz7do1e3t7Z2fntreSBQBQG6o0jE9LS6NpuqSkZNiwYUzHoiAamDIwDq2uEygOKAk0RUVCtVWRvr5+XFzcvn37lixZEhYWVldXx3REAACypErDeAAAAAAACbm5ud28eZMQ4uLicvr0aabDAQCQGQzjAQAAAEA9GRoaJicnJyQkzJs3b8WKFa9evWI6IgAAGcAwHgAAAADU2bRp065evVpSUjJ8+HDc4wAA1IAsHzgXExMjk1WB8uByuRr1oBplQ1EU0yGAasAD52QOBzVQDziOt5OZmbls2bKQkJD169cr7cNiAABeS1uG69KQQ4XmPIVVE3JUfhietYNnpIvC1z1yoiEHNaZozsGUQSivKB8fn/Hjx3/00Ueurq4pKSlvv/020xEBAHQHLqoHAAAAAE1haWl55MiRhQsXTpgwIT4+vrW1lemIAAC6DMN4AAAAANAgFEWFhoZevHgxJydnwoQJDx48YDoiAICuwTAeAAAAADSOnZ3dyZMnfXx8xowZs2vXLqbDAQDoAgzjAQAAAEATaWlprVixoqCgYNeuXR4eHqWlpUxHBAAgEQUN49lsNkVRFEXduXNHMIXP58fFxS1btkzw0ZYtWwTTz549a21t3bNnz/nz58spmMLCwsmTJ//222+Ctz/99FNOTo7gdXR0tCDOkJCQ7q1cczIFlSBofsXFxVu2bFFwC+yk+TFLObskaDK0SXljZE/Y2Ng4aNAgFovFZrPHjRt38eJFor4VVnWDBg26cOHCpEmThg0btn//fqbDAQB4PcWdjc/Ozq6srBTcEbSlpcXX1/e9995LSEiIi4uztbXdsGHD8+fPCSGurq4XLlwICgrau3evPML44YcfUlNTr1+/Lpzi7e199uzZPXv2EEKioqKKi4uXLFkizSY0J1NQcsLmZ2dnFx4ersgW2HnzY5DSdknQWGiT8sbUnrC5uXnUqFHl5eVlZWUDBw708/Mjalph9aCtrR0REZGdnb1x40ZfX99nz54xHREAQGcUN4wfPHiwubm54PUXX3xhYWExcuRIwdvY2Fg9Pb3169crIAw/P78vv/xSX1+/7cSoqKjo6Ojbt29ra2vb2tra2NhIswnNyRSUXLvmRxTYAjtvfvLeeieUtksqIABQTmiT8sbUnpDNZu/du9fQ0NDQ0NDX17e0tFTwsEz1q7A6GTVq1NWrV+3t7Z2dnY8cOcJ0OAAAHWLgt/EtLS1JSUlBQUHCKcbGxjt37tyxY4fonULz8vKGDBnCZrNdXFyOHz8eERFBUdTixYsdHR3ZbHZsbKxgtqysLAcHByMjowULFjQ1NXU1JH19/VmzZsn87ibSZEoIEZuscmYKSki0+ZGOW6CEzY9I1wIZb37y6JJExWsCzMJhQt4Y3xO2trZWVFSkpKTMmTOHoiiidhVWP3p6enFxcZmZmatXrw4KCqqtrWU6IgAAMRgYxl++fLmsrMzZ2bntxJkzZ37wwQeRkZFtJ1ZXV8+aNWvNmjVPnz5dsmTJhx9+uHr1ag6H4+/vf/v27cTExPj4eEJIeXl5QEDAli1biouLb9y4kZyc3I2ohg4dKvOvXaXJtLKyMj4+vl2ySpspKCGxzY+Ia4ESNj8ii77GbPOTeZckql8TYBYOE/LG+J5w2rRplpaW5eXlX3/9tXCiOlVYXY0dO/bKlSv6+vrOzs4FBQVMhwMA0B4Dw/ji4mJdXd1evXq1m56QkPD777+fP39eOCU3N5fD4cybN69Xr15hYWGmpqYnT54UfERR1MSJE3k8XnNz86lTp2xsbDw9PU1MTGbMmHH69OluRGVmZvbo0SPBBW+yIpNMSZtk8/LylDNTUEIdNT8i0gIlbH4y6WvMNj+Zd0k1qAkwC4cJeWN8T3js2LHy8nI3N7dhw4bV1NQIJqpThdVYr169kpOTBVdzhIWF8Xg8piMCAPgfBobxDQ0Nurq6otPNzc23b9++evVq4ZSKigrhj8wJIRwOp6KiQnTBqqqqu3fvCu67zuVyX7x40Y2odHV1W1tbGxsbu7FsRzQnU1BCHTU/ItICJWx+RBYtkNnmJ/MuSVS/JsAsHCbkjfE9YY8ePTgcTmRkZM+ePYVn4NWpwmpv6tSpN2/ebGhoGDFixOXLl5kOBwDg/zAwjGexWB39kGzu3LkcDiczM1Pw1tLSsqqqSvhpeXm5paWl6FLGxsZOTk70/5ebm9uNqJqamrS0tPT09LqxbEc0J1NQQp00P/LPFihh8yOyaIHMNj+Zd0mi+jUBZuEwIW/Ksydse+5dnSqsCXr37p2amhoTE+Pl5RUZGdmNW04AAMgcA8N4W1vbxsbGuro6sZ/u2LEjMTFR8NrNza2ysjI9Pb22tjYpKammpsbNzU10kUmTJhUVFR04cKC+vp7H43Xv5ENVVVX//v0Ft5+RFc3JFJRQ582PtGmBEjY/IosWyGzzk3mXJKpfE2AWDhPyxuCe8Jdfftm1a1djY+PLly+3b99eVVX17rvvCj5SpwprDh8fn2vXrt25c2fkyJHXrl1jOhwA0Hi0jHC5XC6X29GnBgYGxcXFgtfNzc19+vS5cOECTdNbt25ls9kmJibff/+9cOa9e/cuXLhQ8PrEiRNOTk4sFsvFxSU3N3fNmjWEkP79+7948WLQoEGEEH9/f5qmf/zxRwcHB11d3dGjR1++fLmqqsrc3DwhIUE0klWrVgmessZms8eMGSN4AIxg+ooVKwSvv/zyy+DgYGYzpWlabLLKkykogDQ9tG3zo1/XAiVsfrTEfU2S5tc9sqqJrLokreI1gY4oZgeo/IcJ+VF8hWnF7gkvX75sZ2enq6vLYrFGjRqVn58v/EgxFaZxHJePlJQUc3PzuLi45uZmpmMBAM1F0TK6w0p0dLTwX1FsNjsjI2P06NFmZmaEkI0bNz5//nzz5s0y2bQoPp/v7+/v4uKybt06SeZvbm52dHQ8cuSIg4NDSUnJpk2b6uvr9+3bJ3ZmZAoKQ1FS9VB5Nz/SxRYobH6Ojo7d3iJqIkrKmoBYCtsBKu1hQpo2KQm1qTBhotdLCMdxOXn06NH8+fMbGxtTUlLefPNNpsMBAE2kuIvqPT09zc3N79y5QwiJjIx8+PDhxYsX5bStpKQkMzOz8PBwCeePiopau3ato6Pj+vXrbW1td+zYIc3WNSdTUHLybn6kiy1Q2PzkF89roSagbJT2MCGneBQPvR5krn///vn5+SEhIWPHjt22bRu+SAUAxVPQ2XhRfD7/q6++8vPzs7Ozk0kA3ZaRkWFoaOjh4SHh/MgUFEb6s6yq2/w6gpqIwtl4eVDkDlD92qQkUGEFwHFc3h48eBASEsJisXbv3m1tbc10OACgQbSZ2rCOjs7atWuZ2npbc+bMkev6NSdTUEJofqJQE1A2aJPyhgqDnAwYMKCgoGDz5s3Dhg3bsGFDaGgo0xEBgKZg4E71AAAAAABqQFtbOyIiIj8/f+fOnd7e3m0fWwgAID8YxgMAAAAAdJ+Tk9OlS5cGDx7s4uJy+PBhpsMBAPWHYTwAAAAAgFR0dHSio6OzsrIiIiJ8fX1ramqYjggA1Jksb3EXExMjk1WB8uByubg1DoMoimI6BFANuMWdzOGgBuoBx3HFa2hoiImJycjI+P7779977z2mwwEA9YT7GwMAAACogIaGhry8vLS0tOPHj0+YMMHHx2f27NlsNpvpuECM3NzcRYsWubu7b968GX8jAJA5XFQPAAAAoAL09fW9vLwOHjz46NEjHx+fzMzMvn37enl5ZWZmvnr1iuno4B+mTJly48YNQoiLi8vp06eZDgcA1A3OxgMAAACopOfPnx89ejQzM/PChQseHh4+Pj7u7u46OjpMxwX/8+uvv4aGhn744YebNm3S1dVlOhwAUBMYxgMAAACoturq6qysrNTU1Dt37kybNs3Hx8fDw0NbW5vpuIAQQiorKz/++ON79+6lpKQMGzaM6XAAQB1gGA8AAACgJp48eZKVlZWZmVlUVDR79uzAwMBx48bhhqnKIDMzc9myZSEhIevXr8cVEwAgJQzjAQAAANTNo0ePDh8+nJmZ+ejRo9mzZ/v4+GA8z7inT59+9NFH1dXVKSkpb731FtPhAIAKwzAeAAAAQG3dvn374MGDGRkZDQ0NM2fO9PHxcXV1ZToozUXT9Hfffbdu3bpPP/30X//6l5YW7jYNAN2BYTwAAACA+rt161ZmZuaBAweam5t9fX2Dg4MdHR2ZDkpDFRcXh4SEtLS0pKSkDBgwgOlwAED1YBgPAAAAoEEE4/m0tDQ9PT0fHx9/f/8333yT6aA0Tmtra0JCwsaNGzds2BAaGsp0OACgYjCMBwAAANA4ra2tf/zxR2Zm5sGDB01MTHx8fIKCguzt7ZmOS7PcunUrKCiIw+F8//33ffr0YTocAFAZ+EEOAAAAwP9j707jmrjCRoGfhC0b0LAlyE59qWgV3CoqrdbigoraVkBFQcWKWldsRREbpNaCtlpFFG3VCqJFWrWiRXGpVSxoq9C6IKJSZF8UBBKWAHM/zL25eZMQss+EPP8P/sgkM3nOc55z4kkmE4NDpVJ9fX337NlTUVFx8ODBhoaG0aNHjxgxYs+ePVVVVURHZygGDRp0+/btcePGDR069Pjx40SHAwDQG/BpPAAAAAAAQF1dXbm5uampqRkZGQMHDgwMDJwzZw6HwyE6LoNw586dsLCwwYMHHzhwwNramuhwAABkB5/GAwAAAAAAZGRk5Ovre/DgwaqqqqioqLt37w4YMAD/xL6+vp7o6Pq4d955Jz8/393dfciQIZmZmUSHAwAgO/g0HgAAAAAAyNDW1nb58uWMjIwLFy6MGTMmMDDwww8/NDc3JzquvuzWrVsLFy4cPXp0UlISpBoA0BP4NB4AAAAAAMhAo9ECAgJSUlLKyspCQ0MzMjIcHR3xLXw+n+jo+qaxY8fevXuXTqd7eXldv36d6HAAACQFn8YDAAAAAACFNDY2njt3LiMj48aNG++9915oaOjMmTNNTU2JjqsPunjx4ieffDJ16tTdu3czGAyiwwEAkAss4wEAAAAAgHJevXp1/vz5jIyMvLw8f3//wMDAKVOmmJiYEB1Xn9LY2Lhq1ap79+4dO3ZsxIgRRIcDACARWMYDAAAAAAAVVVRU/PzzzxkZGY8fP546dWpgYKC/v7+xsTHRcfUdGRkZq1atWrhwYVxcHJz4AAD4vzAN4fF4RDcFaB6Px9NUhQD5YAQBzYLBqzgYfcDQaGl+ePHixXfffTd27FgbG5ulS5fevHmzu7tbG09kgKqrq2fMmDFkyJCCggKiYwEAkIImL3Gnj/9r5PF4+hi2bsB/bXUMSlE1MIqlweBVFpQQecCI1jbtzQ9OTk5r1qzJycn566+/Bg4cuHHjRhcXF3wLBud+qofD4fz666/r16+fOHFiQkJCV1cX0REBAAgGV6oHAAAAAAAa4+rqiq/es7Ky2Gz2kiVL3Nzc8C1Eh6bfQkND79y5c+nSpXfffbe4uJjocAAARIJlPAAAAAAA0LxBgwbFxsY+fvz4woULbDZ70aJFoi1Eh6avXF1dr169unDhwjFjxuzZswdOcwDAYMEyHgAAAAAAaBG+ei8uLj516hRCaOrUqaItRIemfygUytKlS2/cuJGWljZlypTy8nKiIwIAEACW8QAAAAAAQBfw1fvTp08PHjzY0NDw3nvv4VueP39OdGh6xtPT888//5wwYcKwYcMOHTpEdDgAAF0j3TJ+/fr1pqamMTExRAciz7Fjx9hsNoVC6d+//4sXLzRyzF27drFYLAqF8uabb+bl5WnkmMAA+fv7JyUlqXkQFYbhypUrjYyMjh8/ruZTSygsLJwwYcLFixdVPkJxcXFAQIC1tTWNRnN2dsY/CJJD2QSqP3K1lDoAVPbixQt3d3cKhdLW1oY0NKvg1B/RCKHs7OxJkyax2WxjY2NLS8uBAwdeunRJI+GpRjQJiFOzjTjtdQThqFSqr6/vnj17Kioq8PX8mDFj8C1VVVVER6c3jI2No6Kirly5cuDAgcDAwPr6eqIjAgDoDumW8d9+++28efOIjqIXYWFhR48eRQg9ffrU2dlZzaPxeLzy8vLIyMj4+HgOh/Ps2TMfHx9NhAn6LLxmZN6VlZX16aefqnl8FYbhvn37Ro4cqebzSjh58mRKSso///yjzkGCg4P79ev3+PHjly9f7t27t7KyEmk0geqPXG2kDuiYnIrSR87Ozn/++afopkZmFaShEZ2amhoQEDB58uS7d+8KBIInT55s2rSppKRE5oN10y+iSQC/DrxQKDxz5oxGjqyljiAV8fV8fHz8o0ePBg0ahG+pra0lOjr9MGTIkNzc3DfffHPIkCFnz54lOhwAgI6QbhlvgNLT04kOAegZjdRMXl7ew4cP1T+O9sydO/frr7+m0+kqH0EoFBYUFGzcuNHW1pbJZM6aNWvt2rXIYBIIdEbxitKXsqFQKCrsJb916o/o1tbWyMjIqKio9evXu7u7m5qacjicBQsWLFu2TObjCekXY2PjWbNmTZkyRSNHU60jkP5UmoiRkZGvr+/BgwerqqqioqLu3r371ltv4et5+JC5VzQaLT4+/pdfftmwYUNQUFBDQwPREQEAtI74ZfytW7d8fHwYDIalpeXmzZsl7l26dCmbzabT6QsWLOju7u7o6AgKCmIymTY2NkePHpW4SUj8UVFRFApl+fLlnp6eLBYrLi4OIRQZGUmhUCZOnMhisdzc3DIyMhBCQUFBFArl6dOnlZWVrq6uLBYLIRQcHFxUVOTk5LRy5Uo5zyKRh2nTplEoFFdX18rKyl9++QU/q/D06dMeHh6WlpaLFy/u6OhYtWoVhULJysqaPXt2dHS0brIBtEGizsVrRqKXjxw5QqPRYmJiZJYlQujChQsDBgyg0WhOTk6bNm166623en32K1eueHt7s1gsLy8v0ZmrMjeKrFu3zsTE5LvvvpOueZlDQ3tMTEw8PDwknkVnCVQ5dfhJuRJzhY5TB0SkX6QkJmSJaVxiKkY9lI1EJUiUosxJXmZ4Ek/XU+lKt0JmKV66dMnb25tGow0ePBjfoo1ZRWW5ubn19fUhISEy7yVJv0RHR3d2dqIe/nuAFOsLOR0h58i67AvtMTMzCwgISElJqaysFK3n8S3Nzc1ER0dqo0eP/ueff+zt7YcOHXrt2jWiwwEAaBmmITwej8fjKbtXfX09m83+9ttv+Xz+ixcvPvvsMwzDwsLCNm/ejD9g1apVVVVVxcXFJiYmDx48+OmnnyZPniwQCB48eLBz506JmzoLG8Mw/JQ5/G8Oh3Pz5s3u7u4ff/yRwWDgG5lMZnZ2tkAgSE5OptFoVVVVGIYhhIqLizEMy8/PZzKZGIYJhUKEUFlZGYZhiYmJorPyJEjkgc/nW1panj17Fr83IiIiPz+fTqdnZma+fPly+PDhe/fuxQNLTU1tbGyMj49XoY0qJweoQE62JepcvGYwqV4ODw/Hh490Wba1tZmbm6elpfH5/MjISB8fHznx4MOwrq6OyWSmpaU1NTUlJyczmcyamhqZGzEMGzVqVGpqKoZh8fHxV69exY8jXfMyh4YcDg4OWVlZqqUOw7C8vDxHR0dfX9/Dhw+3tLRg/3vQaSSBMkeumqmTzptSqYPBqxQ56ZL5IiUxIYtXVFVVlcRULLNsZFaCeClKT/Iyu1v66TBZpSvdCpkB1NTU0Gi0ffv2tba24tcPb21txTQ3q+DUGdE//PADQqitrU3mvQT2S2Jiovj/rIRCIf4A1fri/v378jtCzb7Qu/mBz+efOnVq+vTpFhYW06dPP3bsGD6Zg55kZ2c7OTktXboUEgVAH0bwp/FXr15lMBiRkZEMBsPJyWnnzp0SD9i7dy+Xy+3fv7+VlVVzczOLxbp37152dvaAAQM+++wziZuENEGEQqGMGzdOIBDgb8MjhOzt7el0ekREhLW19fXr19U5uEQeGAzG3LlzT5w4gRASCoVCofDx48dOTk7Tp0+3srKaMWPGjRs38B1dXV0tLS2joqLUaxwgUq91LqeXxcuyrKysubl55syZDAZj6tSpz5496/WpL1++zOFw5s2bZ25ujlfytWvXZG4U7bJly5axY8dOmDBBzmE1ODR6NWrUqKdPny5fvvzw4cMeHh737t2Tfow2EtgHUgdQDy9SEhOy+OOvX78uMRXLLJueKkFUitKTPJfLlQ5P+ulEd4mXrnQrZAaQlZXF4XA+/fRTGo2GnwMik5qzijowDEM9n2dObL+I3svbtGmTRGDK9sXnn3+uSEcgQvtClxgMRmBgYGZm5n///RcYGJiRkdGvX7+AgICMjAz8xAogYeLEiffv30cIDRky5ObNm0SHAwDQCoKX8ZWVlQ4ODj3d29zcPGfOHBsbG1NT05qaGoTQ1KlT165du2zZMldX1ytXrkjc1GHgyrGzs3v58qXKu0vnASEUHh6emZnZ3NycnZ390Ucf1dXVPXnyBD8Xl8fjNTY2aih2QDxN1TmXy6XRaL/++mtra+uFCxd6OkdXXE1Nja2tregmh8PBP7KT3oj/nZSUdPLkyfPnzysYkppDQ0FmZmbz5s27devWlClTIiMjVT6OUgnsG6kD0i9SMidkEempWGbZyKkEEYlJXmZ4Cs780q2QGUBVVZWyF21VYVZRh6urK0Lo6dOn0neRp1+2b99ubGzcUxMU6YuCggIVrp6r474gBJvNDg0NzczMLCkpCQwMTElJcXBwwLfgJ18AEUtLy4MHD+7YsSMoKGjjxo3t7e1ERwQA0DCCl/EcDkfOL4ukpKQUFhYWFBS0trZyOByEEIVCiY6OLi8vX7JkycqVKyVu6jBwJWAYVlFRIefdil5J5wEhNGLECA8PjzNnzuCLEzabPXjwYNFZFpcvX9ZQ+IB4mqpzFosVHx//ySefsNns3Nzc/fv397oLl8utq6sT3ayuruZyuTI34n9/9NFH586dS0pKUuTtf/WHRq9aWlpE3xpFCM2cObOsrEzloymVQH1PHcBJv0jJnJBFpKdimWUjpxJEJCZ5meEpOPNLt0JmAGw2W3yjIlSYVdTh6+tra2srcQZ7V1fXli1bSNUvcijSF56ensp2BNJ5XxDLysoKX70XFBQMHz48ISHB3t4e3yI6IxIghD7++ON//vnnyZMnI0aMyM/PJzocAIAmEbyMnzRpUkNDQ1xcXH19vVAolHhta29vNzMzY7FYRUVF+I+mfv/995cvX+7q6ho5ciSFQpG4SVAjetTS0tLW1paYmNjR0YGfKMtisW7duiUUCisqKvDHUKlUKpVaWFgoEAjE98UwrLGxMSIiAsnKAy48PPzo0aNWVlZGRkbjx48vKio6ceIEn88XCATwaXxfIlHnPdVMrwQCQXp6+oMHD9ra2nJzcxX5rMbPz6+2tjYtLa25uTk5ObmhocHPFqSG1gAAIABJREFUz0/mRvzx9vb2AwcO3Llz54IFC5qampCsmkeyhob2HDt27OrVq21tbaWlpUlJSePHj9dqAkUjV83Uycwb0m3qAJL1IiU9IYtXlPRULLNs5FSCOPFJXmZ4Cs780q2QGcD777//5MmT1NTUlpaWCxcuKJIfFWYVddBotKSkpMOHD0dHR5eUlAiFwufPn8fFxQmFQlL1S3t7+8KFC2XepUhffPXVV8p2BNJ5X5CEg4PDmjVrcnJy8vPzRev5iIiInJwc/CsYwM7O7vTp01988cWUKVNiY2O7urqIjggAoCGa+pK9yldM+f3330eMGMFgMBwcHHbv3v3ZZ5+ZmJgwGIxvvvmmtLT0rbfeYjKZc+fOffPNN998881ff/21X79+xsbGHh4ely9fzszMFL+ps7BTUlLYbDZC6H/+53/mzp2LEHJxcWlsbBw0aBBCKCQkBMMwJpNpZWVlYmLi5eV1/fp1fMctW7bQaDQPDw98fb5o0SIMw4KCgszMzBBC0l+BW7hwIYZh0nno6urCMOzVq1cMBqOkpAQ/+E8//eTh4WFmZjZq1Ki///571apVCCF7e/tbt26pkBmVkwNUIyfb0nWO18zcuXMlejkqKsrU1JTBYOD1I1GWbW1to0ePxu+iUCju7u6iypQgPgyzs7MHDx7MYDC8vLxEo0x6444dO6hUqqOjY0dHB/7LCK6urpismpc5NGRat26dk5MTPjRGjx5dUVGhbOo6OjpCQkKcnJyMjY25XO6iRYtevXql2QTu3r27p5GrTupkzhWKpw4Gr1Lkp0viRUrmhCyqKExqKu5p3ElUgswZW2KSl0ni6TZs2CDzJUmiFdIB4Ec7cOCAo6Mjm82eP38+QmjWrFmamlUwTYxo3I0bN6ZMmfLGG29QqVQ2mz127NjMzEwC+0XmJODr66tOX8jpiISEBJlHVrwv+vb8UFJS8t133w0fPtzJyWn16tX4hQCJDooUSktLP/jgg1GjRj1+/JjoWAAAGkD8Mp5Y2gubyWQ+fPhQG0cW6e7uXr16tfaOr6d9qqd0kO36+vqwsLCOjg4Mwzo7O7/44osPP/xQq88ok8aHhs4KlfAEKp46GLxK0Wq61CkbbU/y6tPGoNBN9fa9flG8RQYyPzx48IDH47311lsuLi74ep7oiIjX3d198OBBGxub+Ph4/AMhAID+Iv534/uw7u5uLR355s2bfD4/NjZ25syZWnoK0PdcvXr1xYsXjY2NHR0dRUVFN27csLW1pchSXl6u1UjEh0Z5eTkhMahAOoFDhw7VcQzam1WAlqhWNtKTPDlHCnlmFWVpql/IgwwTFKkMGjQoNjb28ePHFy5cYLPZixYtEm0hOjTCUCiUpUuX3r59+7fffps8ebI6F4sBABAOlvFasWDBAj6fP2XKFJm/bqW+/fv3c7lcCoUCX44Fips2bRqHw8HPO508efLYsWP37dsn8+09R0dHLcUgPTQcHR11HIPKpBO4ceNGnT27tmcVoCWqlY30JE/OkUKGWUU1muoX8iB2giIzfPVeXFx86tSptra2Dz74QLSF6NCI4e7u/vvvv0+fPn348OGHDh0iOhwAgIp6/E0UoI7U1NTU1FTtHf/kyZPaOzjoq5hMJuGVo+2hoVXEJlCvU2fIVCsbwoeqgsgwq6im7/WL/vaFzgwaNCg+Pn779u1//vlnRkbGe++9Z2VlFRgYGBoa6u7uTnR0OkWlUtesWePn5xcaGnr27NkffvihX79+RAcFAFAOfBoPAAAAAAAMApVK9fX13bNnT3l5+cGDBxsaGsaMGYNvqa6uJjo6nRo0aFBeXt64ceNGjhz5yy+/EB0OAEA5sIwHAAAAAACGxcjICF+9V1RUxMbG3r17d+DAgfiW2tpaoqPTERMTk6ioqNOnT2/evDkoKOjVq1dERwQAUBQs4wEAAAAAgIEyMjLy8/NLSUmprKyMioq6e/euh4eHr6/voUOHXr9+TXR0ujBq1Kj8/Hx3d/fBgwefP3+e6HAAAIpR7QL30ng8HtFNAZpnCL9JQxIwgoBmweBVHIw+YGhgfpBPIBCcO3duwYIFVlZW06dPP3bsWFNTE9FB6cLNmzf79++/dOnS5uZmomMBAPRCk5/G9+FXBQP5kVUJ8F9bHTPAGlOQYQ5AdcDgVRYUmM7AcCYczA+9otPpAQEBKSkpZWVloaGhGRkZjo6O+BY+n090dFrk6+t79+5dhNCQIUP++OMPosMBAMgDJ9UDAAAAAAAgicFgBAYGZmZm/vfff4GBgRkZGf369QsKCsrMzOzo6CA6Oq2wsLA4ePBgUlLS/PnzIyIiBAIB0REBAGSDZTwAAAAAAAA9YrPZoaGhmZmZz58/nz59+qFDhxwcHPAtQqGQ6Og0z9/f//79+wKBYOTIkfjn8wAAsoFlPAAAAAAAAL2ztrbGV+8FBQXDhw9PSEiwt7fHt3R2dhIdnSa98cYbqampsbGx06ZN27hxY189+wAA/QXLeAAAAAAAAJTg4OCwZs2anJyce/fu4et5V1dXfAuGYURHpzGBgYEFBQWPHj0aOXLkv//+S3Q4AID/j1zLeH9//6SkJKKjAKDPgiEGAMnBIAVAvzg7O+Or95ycHHd39zVr1ri4uPSl9TyXyz137tz69ev9/PwSEhK6urqIjggAgBDZlvFZWVmffvqp+sfh8Xjl5eXqH4coKsSv7002BGT4+VkYYuqD4amPyDD6FASDlIRUSyZ0gaHBP42/e/duVlYWm81esmQJvqrPyckhOjQNCA0NvXPnzsWLF997772nT58SHQ4AgGTLeE1JT08nOgS1qBC/vjfZEEydOtXLyyspKamuro7oWNRlyPUGw1MfhYaGDhgw4Ntvv62srCQ6Fh2BqtMg1ZIJXWCwBg0aFBsb+/jx4/Pnz7PZ7EWLFuFbioqKiA5NLa6urteuXQsLCxs9evSePXv6xrkGAOgvEi3jjxw5QqPRYmJiEEJRUVEUCmX58uWenp4sFisuLg4hFBkZSaFQJk6cyGKx3NzcMjIygoKCKBTK06dPKysrXV1dWSwWQig4OLioqMjJyWnlypUIoYCAgHXr1hHbNNyVK1e8vb1ZLJaXl9elS5cQQr3GL91kmXuRtslAnEAg+Pfffzds2ODk5DR69OiUlJTm5mZdBiAaYgqOL6RAifaZYtPI8NSvGcmgtLW1FRUVbd682d3dffjw4d9//31DQwPRQckg/3VQtUGKoOp6psjAV+FFGfXReRIoC1+9FxcXnzp1qq2tbcKECfgW/f00m0KhLF269I8//jh+/Li/v39FRQXREQFgwDAN4fF4PB5PzYOEh4dv3rwZ/5vD4dy8ebO7u/vHH39kMBj4RiaTmZ2dLRAIkpOTaTRaVVUVQqi4uBjDsPz8fCaTiWEY/ssfZWVlagYjTv3W1dXVMZnMtLS0pqam5ORkJpNZU1ODYViv8Us3WXovbTQZ01CfAhFvb2/xoWdhYWFmZvb+++8fO3aMz+frJtuiIabg+MIUKFEd0HZyNDg8yZAuDAavlEmTJomPPiaTaWpqOmrUqGPHjjU3N5MqXfJfB0k7SBVHnmwrOPBVeFHGoAuALF1dXTdv3ly9ejWXyx04cCCPx3v+/DnRQalIKBTGx8fb2toePHiQ6FgAMFDG2nyLQAMoFMq4ceMEAkFnZ6exsTFCyN7enk6nR0REfPnll9evXyc6QEVdvnyZw+HMmzcPIRQREbF9+/Zr167NmTNHkX0lmqzgXhrx999/JyQk6Ozp+jaBQCB+s6mpCSH0+++/37lzZ/ny5W5ubsOGDevq6jIyMtJZSL2OL10WG4E0ODy1G6gyHj16BINXpL6+Xvwmn89HCN2+ffvhw4cREREuLi7Dhw8XCoUmJiYEBSiP+DhFhjpItUHlgQ9dAFRDpVJ9fX19fX137dqVm5ubkZExevTo/v37BwYGBgcHc7lcogNUgrGxcVRUlL+/f2ho6JUrV/bv329jY0N0UAAYFrIv4+Wws7N7+fIl0VEoqqamxtbWVnSTw+HU1NQoexDdN7m9vZ2cp57qo+7ubqJDUIJ+jS816enwlK+jowMGr0ifvLQy2UpO76g/8KELgGqMjIxE6/nff/89JSVl69atAwcODAwMnDt3rp2dHdEBKmrIkCF5eXmxsbFDhgw5cODAzJkzRXc9fvz40qVLa9asITA8APo2fV3GYxhWUVHh4OBAdCCK4nK54hc2q66uVvZtV0KaPHbs2NjYWF0+Yx+Gf/FSxMLCor29fcyYMQsXLpw9e/aOHTsQQrr8KF4OvRtfatLT4Smft7c3DF6R/Px88Zv4mdJDhw5dsWLFRx999M033yCEyPlRfE9IWHJ6R82BD10A1GdkZOTn5+fn59fW1nb58uWMjAwej/f222+HhobOmTPHwsKC6AB7R6PR4uPjZ86cGRYWlpaWdvDgQTab3dnZ+fHHHxcXF48aNcrHx4foGAHom0h0iTsFtbS0tLW1JSYmdnR0TJgwgcVi3bp1SygUii6zQaVSqVRqYWGhxDnMxPLz86utrU1LS2tubk5OTm5oaPDz80MIKRK/RJOl9yJnk4FMDAbDzMzMx8cnMTGxrq7u2rVroaGhDAaD6Lj+r16LDfXFetPg8DSEdOkvMzMzMzOzoUOH7tq1q6qqKi8vLzQ0FL8gmR4xzEGqDQoOfBVelGXuBYAcNBotICAgJSWlqqoqKioqJyfHzc0N3yLnargrVqwgyW/fjB49uqCgwN7eftiwYdeuXdu6dWtpaalQKAwICHj16hXR0QHQR2nqS/bqXzElKirK1NSUwWAkJCRs2LABIeTi4tLY2Dho0CCEUEhICIZhTCbTysrKxMTEy8vr+vXrGIZt2bKFRqN5eHhEREQghBYtWoRhWFBQkJmZ2dy5czEMmzZt2po1awhvHYZh2dnZgwcPZjAYXl5ely9fxjf2Gr90k2XupfEmY3AVHE0bM2bMkCFD9u3bV1tbK32vDrItGmL42FdkfGG9laimik0+HSRHU8NTNzNSr2DwSggICPDw8Ni5c2d5ebn0veRJV6+vgyoMUkxXVacg8mQbU3jgq/CijOl8nlQcqboA9KSxsfHYsWPTp0+3sLCYPn36sWPHWlpaxB9QUFBgYmJia2t7584dooKU9ttvv7m5udFoNPx/GmZmZv7+/kQHBUDfRME09KuP+Nmb2j6Hk8Vi3blzZ+DAgVp9Fmm6aZ1MRDUZEdrqPun169eWlpY93UuGbBNYbPKRITkyQcb0RWNj4xtvvNHTvXqULtKWnOL0KNsyQRcAHWtoaMjMzMzIyLhx48bkyZMXLFgwefJkU1PTzz77bO/evUKhkE6nb9u2LTIykuhIEUKovb3d09Pzv//+E60vmExmfHw8/uOXAAAN0r+T6vXrOmEaYYBN7pPkrOHJA4pNWZAxvSBnDa93oOQIB10AdInNZoeGhmZmZhYVFb333ns7duxwcHD45JNPUlNT8d81bG1t/eKLLwIDA1tbW4kOFm3YsEH0w404Pp8fFRVVUFBAYFQA9En6tIxfsGABn8+fMmXKvXv3iI5FRwywyYAoUGzKgowBHYOSIxx0ASAQl8tduXLlzZs38/Pzzc3NxS+7wOfzL1y44O3tXVJSQmCEOTk5iYmJ0u8mtLa2zpgxA/+lTwCApujTMj41NRXDsPLy8mHDhhEdi44YYJMBUaDYlAUZAzoGJUc46AJABo6OjkKhsK2tTXxja2vr8+fPvb29s7OziQoMf/ZVq1a5u7vTaDQmk4lvxzCsrq4uPDycqMAA6JP0aRkPAAAAAACAIcMw7MSJE52dnRLbOzs7m5qaZs2atW3bNk1d+kopLBbLz89vz549z549Kyws3L1797Rp05hMpoWFBYZhGRkZKSkpuo8KgL5KX383HgAAAAAAkFlsbOzWrVuJjsKwtLa2btmyZcuWLUQHIkNYWFhYWBjRUQCgx3g8nugCpbCMBwAAAAAAWiH+n06gEe3t7QKBgMFgmJmZER2LclpbW6urq93c3IgOpBfwaw6AnCRqUpPL+K1bt/bt91z7dutk4vF4RIdgQPr8CFITJEcpMHiVAqNPxyDbxIL5Qa+ZmZnp3QIeR6fTyb+GB0BfaHIZbyBvuBrOW3SG0EZSMZARpA7DGX1qghQpC0af7sFwJopmc97d3U2hUCgUigaPCQAAoFdwiTsAAAAAAANSVVVVWFgo/otlKnv8+PHAgQPT09O7u7vVPxoAAAAFwXfjAQAAAAAMyJ07dz766KPS0lI6ne7o6Ojs7Ozs7Ozo6Ojo6Oji4oL/YWpqqsihysvLS0tLw8PDIyMjv/rqq5CQEBMTE23HDwAAAD6NBwAAAAAwIDNnzsQ/jS8vLz9z5syaNWu8vLza29tzcnK+/PLLgIAAKysrKyurQYMGTZw4MSIiIiEhISUl5cqVK8+fP5f4nbMXL15QqVQ+n19ZWbl69ep+/frt3r27tbWVqKYBjSssLJwwYcLFixeJDgRoUXZ29qRJk9hstrGxsaWl5cCBAy9dukR0UKAX8Gk8AAAAAIAhotPp7u7u7u7uEtsxDKuqqiorKysvLy8rKystLf3777/Ly8tfvHhRX1/P5XKdnZ2dnJycnJz+++8/0aK9ubm5ubl5y5YtsbGxGzduXLNmjc4b1JfxeLxPPvnE0dFRl0c4efLkv//++88//6j8pID8UlNTlyxZsn379uTkZEdHx4aGhuzs7JKSEs0+i/oFrKmD9Bk6+jSexWLhV0B5/PgxvkUoFMbHx69atQq/a9euXfj2nJwc/FSuRYsWaSkYibcVf/7556ysLPzv2NhYPM6FCxeqdnDDaSkgM3JWHdlAlgCBoPyIQmDmcWvWrNm4cSMid+YpFEq/fv1GjRr18ccfr127dvfu3RkZGbm5uRUVFS0tLTdu3Pj666+nTZtmZWVVWloq8a14Pp/f1NS0bds2Lpd748aN9vZ2olrRx6Snp+v+CHPnzv3666/pdLqaTw1Iq7W1NTIyMioqav369e7u7qamphwOZ8GCBcuWLdPsE6lfwJo6SJ+hu5PqMzMza2trBwwYgBDq6uoKCgr64IMPEhMT4+PjXV1dt23b9urVK4SQr69vXl5eaGjo0aNHtRHGyZMnU1JSxN9WnD17dk5OzpEjRxBCMTExJSUlK1asUOcpDKelgJxIW3WkAlkCBILyIwqBmcf99ddfqamp+N96mnkTExMXFxdfX9+QkJCoqCiZV7ajUqkmJiatra137969fv16c3Oz7uPUvVu3bvn4+DAYDEtLy82bNyOErly54u3tzWKxvLy88FOUo6KiKBTK8uXLPT09WSxWXFxcT/suXbqUzWbT6fQFCxZ0d3cHBwcXFRU5OTmtXLny9OnTHh4elpaWixcv7ujo6OmYco6AEJI4CDEpAySQm5tbX18fEhIi816JGtZIASOp8lOhhqGAkS6X8W+//batrS3+9/bt2+3s7EaOHInfjIuLo9FoX375pQ7CkPm2YkxMTGxs7KNHj4yNjV1dXZ2cnNR5CsNpKSAn0ladDgJQHGQJEAjKjygEZh4h1NnZeeTIEX9/f9GWPpD5iooK/A9TU1Nzc3NTU9NBgwatXr06JSWlsrJy3bp1kydPNjc3JzZIHXj58mVAQEBQUFB9ff2DBw86Ojrq6+tnzZq1YcOGqqqqFStWfPzxx7W1tQkJCRwOJyQk5NGjR0lJSQkJCTL3RQjRaLTCwsL79++np6cXFhampaUhhMrKymJiYubPn79r166SkpJ///334MGDMo8p5wj79u2rrq6WOAhxmQMEw0+ed3V1lb5LuobXr1+vZgHLLD9la1h6FOgqW+RCwCXuurq6kpOTQ0NDRVvYbPaBAwf279//7NkziQcr+CaQmm/J0On0WbNmHTp0SJ12SVOnpaiHt2zJ2VJAHlB1itBGlpB6iSJhloCWQPkRhfDpce/evREREeI/sd4HMt/Q0PDGG29Mnjz566+/zs7ObmlpefDgwe7du2fMmCH6SMMQXL16lcFgREZGMhgMJyennTt3Xr58mcPhzJs3z9zcPCIiwtra+tq1a6LHUyiUcePGCQSCzs5O6X0RQnv37uVyuf3797eyshI/neH69etOTk7Tp0+3srKaMWPGjRs3ZB5TzhHkHwQYGgzDEELi85KInBpWuYCRJmoYChhHwDL+77//rqysHDJkiPjGmTNnfvjhh/i3xUQUfBNII+8pDh069Ny5c+q0S5o6LZX5li1pWwrIA6pOERrPEtLERES2LAEtgfIjCrHTY0lJSX19vbe3t8R2vc58d3d3YWFhQ0PDxYsXIyMjfXx8DPbX5iorKx0cHMS31NTUiL+RweFwampqFNy3ubl5zpw5NjY2pqamEnvV1dU9efIEv7wRj8drbGyUeUw5R1D8IMAQ4J/DP336VPouBWtYqQJGmqhhKGAcAcv4kpISMzMz6TOsEhMT//jjj9zcXNEWBd8E0shbMjY2NqWlpfg7UpqikZYiscZeuXKFnC0F5AFVpwiNZ0kjExHZsgS0BMqPKMROj3FxcRJvFuD0OvNUKtXFxYXoKEiBw+FUVVWJb+FyuXV1daKb1dXVXC5XwX1TUlIKCwsLCgpaW1s5HI74XWw2e/Dgwdj/c/nyZZnHlHMExQ8CDIGvr6+trW1iYqL4xq6uri1btihYw0oVMNJEDUMB4whYxre2tpqZmUlvt7W13bt37/r160VbFHwTSCNvyZiZmXV3d7e1tamwb08Mp6WAPKDqFKHxLCFNJIpsWQJaAuVHFAKnx7S0tEmTJllYWEjfZQiZNwSTJk1qaGiIi4urr68XCoVVVVV+fn61tbVpaWnNzc3JyckNDQ1+fn4K7tve3m5mZsZisYqKivDaoFKpVCq1sLDwnXfeKSoqOnHiBJ/PFwgEPVWdnCMIBILx48crchBgCGg0WlJS0uHDh6Ojo0tKSoRC4fPnz+Pi4oRCoYI1rFQBK15+6o+CPo+AZTyDwejp+2Nz5szhcDgZGRn4TQXfBNLIWzIdHR1UKpVGo6mwb08Mp6WAPKDqFKHxLCFNJIpsWQJaAuVHFAKnx+PHj8+bNw9f8KelpSUkJPj4+OB3GULmDYG1tXVmZmZmZqaLi4ubm1t6erqNjc3p06cTEhK4XG5ycvKZM2esra2joqJqamrmz5//+vXr6dOnI4QWLlwove/s2bObmpocHR23bdtmY2Mzf/58hNDs2bMDAgJiY2NTUlK2bt1qbW09YcKEZ8+eyTymnCMsWbLE0dFR4iA9tSsyMtLZ2bmioiIwMHDMmDGVlZW6yijQncDAwKtXr+bn5w8bNoxGo40YMeLq1au+vr7SNbxjxw41C1hm+Slbw9KjgNgEEsVY90/p6ura1tbW0tLCYrGk792/f/+77747fvx4hJCfn98nn3ySlpY2Y8aMtLS0nt4EGj9+fHh4+IkTJ2bOnEmhUDo6Ot544w1lo6qrq3NxcZF5gQeVaaOlERERJGwpIA+oOkVoPEtIExMR2bIEtATKjygETo/ivw8/f/58R0fH+Ph4/KYhZN5AjB8//q+//hLfMnHixH///Vd8S0JCgugq3A8ePJCz7+PHjyWOL/6L2cHBwaK/hw8fLvOYvR5B/CA92bVr165du3p9GNB37777rvg0JSJRw35+fpoqYI3UsLwmGQACPo0fMWJEv379Hj58iBD67rvvNm3aFBYWdvjwYfxee3v7mJgY/G8F3wSSflOnvr7ezs5u37590s/e09uK9+/fnzFjBnlaKvMt25iYGHK2FJAHVJ0iNJ4lpSYifckS0BIoP6IQOz32xBAyD0iuvLycIkt5eTnRoQEAekTR1FVVYmNjRf9KY7FY6enpo0aNsrGxQQh99dVXr169+vbbbzXy1NKEQmFISIiXl9fmzZsVeXxnZ6enp+e5c+c8PDzKy8t37NjB5/N//PFHmQ+GlgJt0GC2SVt1np6eaj61HmUJKZkoDWYJweBVku7T1bfLT0GEVGkfnh4Vp8vMw1wE9BHULSAnicrU3afx06dPt7W1xc+O2Lhx4/Pnz2/fvq2l50pOTraxsYmMjFTw8TExMdHR0Z6enl9++aWrq+v+/fvVeXbDaSkgJ9JWnZbiUY22s4SUTBQ5swS0BMqPKDA9AgAA6Bt09N34lpYW8ZtGRkanTp3auXOnnZ2dm5ubxp9u1apVij84PT193Lhx/v7+CKHY2Fg133sznJYC0iJt1ZGKtrOElEkUabMEtATKjygwPQIAAOgbCLjEHc7ExCQ6OpqoZxen7Qs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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "preprocessed_inputs_cat = layers.Concatenate()(preprocessed_inputs)\n",
        "\n",
        "titanic_preprocessing = tf.keras.Model(inputs, preprocessed_inputs_cat)\n",
        "\n",
        "tf.keras.utils.plot_model(model = titanic_preprocessing , rankdir=\"LR\", dpi=72, show_shapes=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PNHxrNW8vdda"
      },
      "source": [
        "此 `model` 仅包含输入预处理。您可以运行它以查看其对您的数据进行了哪些操作。Keras 模型不会自动转换 Pandas <code>DataFrames</code>，因为不清楚是应该将其转换为一个张量还是张量字典。因此，将其转换为张量字典："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "5YjdYyMEacwQ"
      },
      "outputs": [],
      "source": [
        "titanic_features_dict = {name: np.array(value)\n",
        "                         for name, value in titanic_features.items()}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0nKJYoPByada"
      },
      "source": [
        "切出第一个训练样本并将其传递给此预处理模型，您会看到数字特征和字符串独热全部串联在一起："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "SjnmU8PSv8T3",
        "outputId": "3a2489a0-fd54-42fd-dfd2-376d11e0602b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(1, 28), dtype=float32, numpy=\n",
              "array([[-0.61 ,  0.395, -0.479, -0.497,  0.   ,  0.   ,  1.   ,  0.   ,\n",
              "         0.   ,  0.   ,  1.   ,  0.   ,  0.   ,  0.   ,  0.   ,  0.   ,\n",
              "         0.   ,  0.   ,  0.   ,  1.   ,  0.   ,  0.   ,  0.   ,  1.   ,\n",
              "         0.   ,  0.   ,  1.   ,  0.   ]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "features_dict = {name:values[:1] for name, values in titanic_features_dict.items()}\n",
        "titanic_preprocessing(features_dict)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qkBf4LvmzMDp"
      },
      "source": [
        "接下来，在此基础上构建模型："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "coIPtGaCzUV7"
      },
      "outputs": [],
      "source": [
        "def titanic_model(preprocessing_head, inputs):\n",
        "  body = tf.keras.Sequential([\n",
        "    layers.Dense(64, activation='relu'),\n",
        "    layers.Dense(1)\n",
        "  ])\n",
        "\n",
        "  preprocessed_inputs = preprocessing_head(inputs)\n",
        "  result = body(preprocessed_inputs)\n",
        "  model = tf.keras.Model(inputs, result)\n",
        "\n",
        "  model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
        "                optimizer=tf.keras.optimizers.Adam())\n",
        "  return model\n",
        "\n",
        "titanic_model = titanic_model(titanic_preprocessing, inputs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LK5uBQQF2KbZ"
      },
      "source": [
        "训练模型时，将特征字典作为 `x` 传递，将标签作为 `y` 传递。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "D1gVfwJ61ejz",
        "outputId": "efb62c08-e03e-4759-a0e5-f6aeedbb6978",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "20/20 [==============================] - 1s 4ms/step - loss: 0.6010\n",
            "Epoch 2/10\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.5433\n",
            "Epoch 3/10\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5048\n",
            "Epoch 4/10\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.4764\n",
            "Epoch 5/10\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4569\n",
            "Epoch 6/10\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4412\n",
            "Epoch 7/10\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4301\n",
            "Epoch 8/10\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4230\n",
            "Epoch 9/10\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.4169\n",
            "Epoch 10/10\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.4132\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7a8b36fef430>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "titanic_model.fit(x=titanic_features_dict, y=titanic_labels, epochs=10)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_preprocessing"
      ],
      "metadata": {
        "id": "UZwjyervdeYR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LxgJarZk3bfH"
      },
      "source": [
        "由于预处理是模型的一部分，您可以保存模型并将其重新加载到其他地方并获得相同的结果："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "Ay-8ymNA2ZCh",
        "outputId": "779ab2b3-fb8d-4a2d-94ad-e6df7227f3dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        }
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-33-1aca526e2de3>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtitanic_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'titanic_model.keras'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mreloaded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'titanic_model.keras'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     68\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0;31m# `tf.debugging.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/layers/preprocessing/index_lookup.py\u001b[0m in \u001b[0;36msave_assets\u001b[0;34m(self, dir_path)\u001b[0m\n\u001b[1;32m    837\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    838\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msave_assets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdir_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 839\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput_vocabulary\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    840\u001b[0m             \u001b[0;31m# Vocab saved in config.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    841\u001b[0m             \u001b[0;31m# TODO: consider unifying both paths.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
          ]
        }
      ],
      "source": [
        "# BUG\n",
        "titanic_model.save('titanic_model.keras')\n",
        "reloaded = tf.keras.models.load_model('titanic_model.keras')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qm6jMTpD20lK"
      },
      "outputs": [],
      "source": [
        "features_dict = {name:values[:1] for name, values in titanic_features_dict.items()}\n",
        "\n",
        "before = titanic_model(features_dict)\n",
        "after = reloaded(features_dict)\n",
        "assert (before-after)<1e-3\n",
        "print(before)\n",
        "print(after)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7VsPlxIRZpXf"
      },
      "source": [
        "## 使用 tf.data\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NyVDCwGzR5HW"
      },
      "source": [
        "在前一部分中，您在训练模型时依赖了模型的内置数据乱序和批处理。\n",
        "\n",
        "如果您需要对输入数据流水线进行更多控制或需要使用不易放入内存的数据：请使用 `tf.data`。\n",
        "\n",
        "有关更多示例，请参阅 [`tf.data`：构建 TensorFlow 输入流水线](../../guide/data.ipynb)指南。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gP5Y1jM2Sor0"
      },
      "source": [
        "### 有关内存数据\n",
        "\n",
        "作为将 `tf.data` 应用于 CSV 数据的第一个样本，请考虑使用以下代码手动切分上一个部分中的特征字典。对于每个索引，它会为每个特征获取该索引：\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "i8wE-MVuVu7_"
      },
      "outputs": [],
      "source": [
        "import itertools\n",
        "\n",
        "def slices(features):\n",
        "  for i in itertools.count():\n",
        "    # For each feature take index `i`\n",
        "    example = {name:values[i] for name, values in features.items()}\n",
        "    yield example"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cQ3RTbS9YEal"
      },
      "source": [
        "运行此代码并打印第一个样本："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "Wwq8XK88WwFk",
        "outputId": "07f922a0-b6b9-48fc-ab4e-74d57f6ee25e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sex                : male\n",
            "age                : 22.0\n",
            "n_siblings_spouses : 1\n",
            "parch              : 0\n",
            "fare               : 7.25\n",
            "class              : Third\n",
            "deck               : unknown\n",
            "embark_town        : Southampton\n",
            "alone              : n\n"
          ]
        }
      ],
      "source": [
        "for example in slices(titanic_features_dict):\n",
        "  for name, value in example.items():\n",
        "    print(f\"{name:19s}: {value}\")\n",
        "  break"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vvp8Dct6YOIE"
      },
      "source": [
        "内存数据加载程序中最基本的 `tf.data.Dataset` 是 `Dataset.from_tensor_slices` 构造函数。这会返回一个 `tf.data.Dataset`，它将在 TensorFlow 中实现上述 `slices` 函数的泛化版本。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "2gEJthslYxeV"
      },
      "outputs": [],
      "source": [
        "features_ds = tf.data.Dataset.from_tensor_slices(titanic_features_dict)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ZC0rTpMZMZK"
      },
      "source": [
        "您可以像任何其他 Python 可迭代对象一样迭代 `tf.data.Dataset`："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "gOHbiefaY4ag",
        "outputId": "af714ca6-0d92-428f-9610-fe44b5ad0fa0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sex                : b'male'\n",
            "age                : 22.0\n",
            "n_siblings_spouses : 1\n",
            "parch              : 0\n",
            "fare               : 7.25\n",
            "class              : b'Third'\n",
            "deck               : b'unknown'\n",
            "embark_town        : b'Southampton'\n",
            "alone              : b'n'\n"
          ]
        }
      ],
      "source": [
        "for example in features_ds:\n",
        "  for name, value in example.items():\n",
        "    print(f\"{name:19s}: {value}\")\n",
        "  break"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uwcFoVJWZY5F"
      },
      "source": [
        "`from_tensor_slices` 函数可以处理嵌套字典或元组的任何结构。以下代码创建了一个 `(features_dict, labels)` 对的数据集："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "xIHGBy76Zcrx"
      },
      "outputs": [],
      "source": [
        "titanic_ds = tf.data.Dataset.from_tensor_slices((titanic_features_dict, titanic_labels))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gQwxitt8c2GK"
      },
      "source": [
        "要使用此 `Dataset` 训练模型，您至少需要对数据进行 `shuffle` 和 `batch`。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "SbJcbldhddeC"
      },
      "outputs": [],
      "source": [
        "titanic_batches = titanic_ds.shuffle(len(titanic_labels)).batch(32)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-4FRqhRFuoJx"
      },
      "source": [
        "不是将 `features` 和 `labels` 传递给 `Model.fit`，而是传递数据集："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "8yXkNPumdBtB",
        "outputId": "68a51103-08c8-4363-dafd-92a3e023deb6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "20/20 [==============================] - 1s 3ms/step - loss: 0.4099\n",
            "Epoch 2/5\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4068\n",
            "Epoch 3/5\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.4041\n",
            "Epoch 4/5\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.4017\n",
            "Epoch 5/5\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.4007\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7a8b3613f6a0>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "titanic_model.fit(titanic_batches, epochs=5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qXuibiv9exT7"
      },
      "source": [
        "### 从单个文件\n",
        "\n",
        "到目前为止，本教程已经使用了内存数据。`tf.data` 是用于构建数据流水线的高度可扩展的工具包，并提供了一些用于处理加载 CSV 文件的函数。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "Ncf5t6tgL5ZI",
        "outputId": "d05b983c-fb53-4271-d0be-e89bd230b85c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tf-datasets/titanic/train.csv\n",
            "30874/30874 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "titanic_file_path = tf.keras.utils.get_file(\"train.csv\", \"https://storage.googleapis.com/tf-datasets/titanic/train.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t4N-plO4tDXd"
      },
      "source": [
        "现在，从文件中读取 CSV 数据并创建一个 `tf.data.Dataset`。\n",
        "\n",
        "（有关完整文档，请参阅 `tf.data.experimental.make_csv_dataset`）\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "yIbUscB9sqha",
        "outputId": "d1b9cadc-e707-45b8-e83f-15d8df9ffa22",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/tensorflow/python/data/experimental/ops/readers.py:573: ignore_errors (from tensorflow.python.data.experimental.ops.error_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.data.Dataset.ignore_errors` instead.\n"
          ]
        }
      ],
      "source": [
        "titanic_csv_ds = tf.data.experimental.make_csv_dataset(\n",
        "    titanic_file_path,\n",
        "    batch_size=5, # Artificially small to make examples easier to show.\n",
        "    label_name='survived',\n",
        "    num_epochs=1,\n",
        "    ignore_errors=True,)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sf3v3BKgy4AG"
      },
      "source": [
        "此函数包括许多方便的功能，因此很容易处理数据。这包括：\n",
        "\n",
        "- 使用列标题作为字典键。\n",
        "- 自动确定每列的类型。\n",
        "\n",
        "小心：请确保在 `tf.data.experimental.make_csv_dataset` 中设置 `num_epochs` 参数，否则 `tf.data.Dataset` 的默认行为是无限循环。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "v4oMO9MIxgTG",
        "outputId": "d7837560-7646-4e12-ec41-a81791b3be54",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sex                 : [b'female' b'male' b'male' b'male' b'female']\n",
            "age                 : [28. 19. 14. 28. 36.]\n",
            "n_siblings_spouses  : [0 3 5 1 1]\n",
            "parch               : [0 2 2 0 0]\n",
            "fare                : [  7.879 263.     46.9    16.1    17.4  ]\n",
            "class               : [b'Third' b'First' b'Third' b'Third' b'Third']\n",
            "deck                : [b'unknown' b'C' b'unknown' b'unknown' b'unknown']\n",
            "embark_town         : [b'Queenstown' b'Southampton' b'Southampton' b'Southampton' b'Southampton']\n",
            "alone               : [b'y' b'n' b'n' b'n' b'n']\n",
            "\n",
            "label               : [1 0 0 0 1]\n"
          ]
        }
      ],
      "source": [
        "for batch, label in titanic_csv_ds.take(1):\n",
        "  for key, value in batch.items():\n",
        "    print(f\"{key:20s}: {value}\")\n",
        "  print()\n",
        "  print(f\"{'label':20s}: {label}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k-TgA6o2Ja6U"
      },
      "source": [
        "注：如果您运行两次上述代码单元，它将产生不同的结果。`tf.data.experimental.make_csv_dataset` 的默认设置包括 `shuffle_buffer_size=1000`，这对于这个小型数据集来说已经绰绰有余，但可能不适用于实际的数据集。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d6uviU_KCCWD"
      },
      "source": [
        "它还可以对数据进行即时解压。下面是一个用 gzip 压缩的 CSV 文件，其中包含 [Metro Interstate Traffic Dataset](https://archive.ics.uci.edu/ml/datasets/Metro+Interstate+Traffic+Volume)。\n",
        "\n",
        "![字体](https://github.com/tensorflow/docs-l10n/blob/master/site/zh-cn/tutorials/load_data/images/csv/fonts.jpg?raw=1)\n",
        "\n",
        "图片[来自 Wikimedia](https://commons.wikimedia.org/wiki/File:Trafficjam.jpg)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "kT7oZI2E46Q8",
        "outputId": "b0fc8158-37fc-4667-ae26-5ed3ed44ce7f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://archive.ics.uci.edu/ml/machine-learning-databases/00492/Metro_Interstate_Traffic_Volume.csv.gz\n",
            "   8192/Unknown - 0s 0us/step"
          ]
        }
      ],
      "source": [
        "traffic_volume_csv_gz = tf.keras.utils.get_file(\n",
        "    'Metro_Interstate_Traffic_Volume.csv.gz',\n",
        "    \"https://archive.ics.uci.edu/ml/machine-learning-databases/00492/Metro_Interstate_Traffic_Volume.csv.gz\",\n",
        "    cache_dir='.', cache_subdir='traffic')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F-IOsFHbCw0i"
      },
      "source": [
        "将 `compression_type` 参数设置为直接从压缩文件中读取："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "ar0MPEVJ5NeA",
        "outputId": "f93a6337-8534-4116-f1fb-938dda20b72f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "holiday             : [b'None' b'None' b'None' b'None' b'None']\n",
            "temp                : [287.1  292.81 271.6  288.33 296.83]\n",
            "rain_1h             : [0. 0. 0. 0. 0.]\n",
            "snow_1h             : [0. 0. 0. 0. 0.]\n",
            "clouds_all          : [90  8 90  1  1]\n",
            "weather_main        : [b'Mist' b'Mist' b'Clouds' b'Clear' b'Clear']\n",
            "weather_description : [b'mist' b'mist' b'overcast clouds' b'sky is clear' b'sky is clear']\n",
            "date_time           : [b'2013-05-22 06:00:00' b'2013-08-01 10:00:00' b'2013-03-26 00:00:00'\n",
            " b'2013-05-24 14:00:00' b'2013-10-10 13:00:00']\n",
            "\n",
            "label               : [5995 5096  519 5550 5078]\n"
          ]
        }
      ],
      "source": [
        "traffic_volume_csv_gz_ds = tf.data.experimental.make_csv_dataset(\n",
        "    traffic_volume_csv_gz,\n",
        "    batch_size=256,\n",
        "    label_name='traffic_volume',\n",
        "    num_epochs=1,\n",
        "    compression_type=\"GZIP\")\n",
        "\n",
        "for batch, label in traffic_volume_csv_gz_ds.take(1):\n",
        "  for key, value in batch.items():\n",
        "    print(f\"{key:20s}: {value[:5]}\")\n",
        "  print()\n",
        "  print(f\"{'label':20s}: {label[:5]}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p12Y6tGq8D6M"
      },
      "source": [
        "注：如果需要在 `tf.data` 流水线中解析这些日期时间字符串，您可以使用 `tfa.text.parse_time`。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EtrAXzYGP3l0"
      },
      "source": [
        "### 缓存"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fN2dL_LRP83r"
      },
      "source": [
        "解析 CSV 数据有一些开销。对于小型模型，这可能是训练的瓶颈。\n",
        "\n",
        "根据您的用例，使用 `Dataset.cache` 或 `tf.data.Dataset.snapshot` 可能是个好主意，这样 CSV 数据仅会在第一个周期进行解析。\n",
        "\n",
        "`cache` 和 `snapshot` 方法的主要区别在于 `cache` 文件只能由创建它们的 TensorFlow 进程使用，而 `snapshot` 文件可以被其他进程读取。\n",
        "\n",
        "例如，在没有缓存的情况下迭代 `traffic_volume_csv_gz_ds` 20 次可能需要大约 15 秒，而使用缓存大约需要 2 秒。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "Qk38Sw4MO4eh",
        "outputId": "50ef3ee3-04de-4386-8aac-4bae57c9be67",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "...............................................................................................\n",
            "CPU times: user 28.3 s, sys: 4.05 s, total: 32.3 s\n",
            "Wall time: 26.3 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "for i, (batch, label) in enumerate(traffic_volume_csv_gz_ds.repeat(20)):\n",
        "  if i % 40 == 0:\n",
        "    print('.', end='')\n",
        "print()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pN3HtDONh5TX"
      },
      "source": [
        "注：`Dataset.cache` 会存储第一个周期的数据并按顺序回放。因此，使用 `cache` 方法会停用流水线中较早的任何重排。下面，在 `Dataset.cache` 之后重新添加了 `Dataset.shuffle`。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "r5Jj72MrPbnh",
        "outputId": "3d6692c4-2a9c-4e39-b54b-adc571771112",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "...............................................................................................\n",
            "CPU times: user 3.08 s, sys: 297 ms, total: 3.37 s\n",
            "Wall time: 2.89 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "caching = traffic_volume_csv_gz_ds.cache().shuffle(1000)\n",
        "\n",
        "for i, (batch, label) in enumerate(caching.shuffle(1000).repeat(20)):\n",
        "  if i % 40 == 0:\n",
        "    print('.', end='')\n",
        "print()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wN7uUBjmgNZ9"
      },
      "source": [
        "注：`tf.data.Dataset.snapshot` 文件用于在使用时*临时*存储数据集。这*不是*长期存储的格式。文件格式被视为内部详细信息，无法在 TensorFlow 各版本之间保证。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "PHGD1E8ktUvW",
        "outputId": "6dd8ec53-40b3-45ae-9243-749307cc36d8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "...............................................................................................\n",
            "CPU times: user 4.54 s, sys: 382 ms, total: 4.92 s\n",
            "Wall time: 4.62 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "snapshotting = traffic_volume_csv_gz_ds.snapshot('titanic.tfsnap').shuffle(1000)\n",
        "\n",
        "for i, (batch, label) in enumerate(snapshotting.shuffle(1000).repeat(20)):\n",
        "  if i % 40 == 0:\n",
        "    print('.', end='')\n",
        "print()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fUSSegnMCGRz"
      },
      "source": [
        "如果加载 CSV 文件减慢了数据加载速度，并且 `Dataset.cache` 和 `tf.data.Dataset.snapshot` 不足以满足您的用例，请考虑将数据重新编码为更简化的格式。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M0iGXv9pC5kr"
      },
      "source": [
        "### 多个文件"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9FFzHQrCDH4w"
      },
      "source": [
        "到目前为止，本部分中的所有示例都可以在没有 `tf.data` 的情况下轻松完成。处理文件集合时，`tf.data` 可以真正简化事情。\n",
        "\n",
        "例如，将 [Character Font Images](https://archive.ics.uci.edu/ml/datasets/Character+Font+Images) 数据集作为 CSV 文件的集合分发，每种字体一个集合。\n",
        "\n",
        "![Fonts](https://github.com/tensorflow/docs-l10n/blob/master/site/zh-cn/tutorials/load_data/images/csv/fonts.jpg?raw=true)\n",
        "\n",
        "图像作者：<a href=\"https://pixabay.com/users/wilhei-883152/?utm_source=link-attribution&amp;utm_medium=referral&amp;utm_campaign=image&amp;utm_content=705667\">Willi Heidelbach</a>，来源：<a href=\"https://pixabay.com/?utm_source=link-attribution&amp;utm_medium=referral&amp;utm_campaign=image&amp;utm_content=705667\">Pixabay</a>\n",
        "\n",
        "下载数据集，并检查里面的文件："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "RmVknMdJh5ks",
        "outputId": "c2b61a5e-15f6-4f26-b025-c3a29aee655e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://archive.ics.uci.edu/ml/machine-learning-databases/00417/fonts.zip\n",
            "   8192/Unknown - 0s 0us/step"
          ]
        }
      ],
      "source": [
        "fonts_zip = tf.keras.utils.get_file(\n",
        "    'fonts.zip',  \"https://archive.ics.uci.edu/ml/machine-learning-databases/00417/fonts.zip\",\n",
        "    cache_dir='.', cache_subdir='fonts',\n",
        "    extract=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "xsDlMCnyi55e",
        "outputId": "6c71c14b-3fd7-4a6d-838e-e7b731a3403e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['fonts/AGENCY.csv',\n",
              " 'fonts/ARIAL.csv',\n",
              " 'fonts/BAITI.csv',\n",
              " 'fonts/BANKGOTHIC.csv',\n",
              " 'fonts/BASKERVILLE.csv',\n",
              " 'fonts/BAUHAUS.csv',\n",
              " 'fonts/BELL.csv',\n",
              " 'fonts/BERLIN.csv',\n",
              " 'fonts/BERNARD.csv',\n",
              " 'fonts/BITSTREAMVERA.csv']"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "import pathlib\n",
        "font_csvs =  sorted(str(p) for p in pathlib.Path('fonts').glob(\"*.csv\"))\n",
        "\n",
        "font_csvs[:10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "lRAEJx9ROAGl",
        "outputId": "67f2096c-7538-46f6-f6b3-f3fe6cb0c979",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "153"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "len(font_csvs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "19Udrw9iG-FS"
      },
      "source": [
        "在处理一堆文件时，可以将 glob 样式的 `file_pattern` 传递给 `tf.data.experimental.make_csv_dataset` 函数。每次迭代都会重排文件的顺序。\n",
        "\n",
        "使用 `num_parallel_reads` 参数设置并行读取并交错在一起的文件数量。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "6TSUNdT6iG58"
      },
      "outputs": [],
      "source": [
        "fonts_ds = tf.data.experimental.make_csv_dataset(\n",
        "    file_pattern = \"fonts/*.csv\",\n",
        "    batch_size=10, num_epochs=1,\n",
        "    num_parallel_reads=20,\n",
        "    shuffle_buffer_size=10000)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XMoexinLHYFa"
      },
      "source": [
        "这些 CSV 文件会将图像展平成一行。列名的格式为 `r{row}c{column}`。下面是第一个批次："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "RmFvBWxxi3pq",
        "outputId": "8da8e230-f9b8-497f-8f05-a7f2e70dd946",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "font                : [b'PALACE' b'CREDITCARD' b'GIGI' b'CONSOLAS' b'STYLUS' b'SNAP'\n",
            " b'CREDITCARD' b'SWIS721' b'WIDE' b'FOOTLIGHT']\n",
            "fontVariant         : [b'PALACE SCRIPT MT' b'scanned' b'GIGI' b'CONSOLAS' b'STYLUS BT'\n",
            " b'SNAP ITC' b'scanned' b'SWIS721 LTEX BT' b'WIDE LATIN'\n",
            " b'FOOTLIGHT MT LIGHT']\n",
            "m_label             : [ 100   48  191 8358  183   95   56  207 8221   93]\n",
            "strength            : [0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4]\n",
            "italic              : [1 0 0 0 1 0 0 1 1 1]\n",
            "orientation         : [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            "m_top               : [36  0 40 39 57 91  0 23 39 31]\n",
            "m_left              : [18  0 24 20 35 20  0 26 35 20]\n",
            "originalH           : [29 45 46 43  7  7 46 61 21 54]\n",
            "originalW           : [32 33 26 37  7 34 29 35 44 29]\n",
            "h                   : [20 20 20 20 20 20 20 20 20 20]\n",
            "w                   : [20 20 20 20 20 20 20 20 20 20]\n",
            "r0c0                : [  1 242   1   1   1 255 106   1   1   1]\n",
            "r0c1                : [  1 242   1   1   1 255 107   1   1   1]\n",
            "r0c2                : [  1 241   1  76   1 255 109   1   1   1]\n",
            "r0c3                : [  1 239   1 255   1 255 112   1   1   1]\n",
            "...\n",
            "[total: 412 features]\n"
          ]
        }
      ],
      "source": [
        "for features in fonts_ds.take(1):\n",
        "  for i, (name, value) in enumerate(features.items()):\n",
        "    if i>15:\n",
        "      break\n",
        "    print(f\"{name:20s}: {value}\")\n",
        "print('...')\n",
        "print(f\"[total: {len(features)} features]\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xrC3sKdeOhb5"
      },
      "source": [
        "#### 可选：打包字段\n",
        "\n",
        "您可能不想像这样在单独的列中处理每个像素。在尝试使用此数据集之前，请务必将像素打包到图像张量中。\n",
        "\n",
        "下面是解析列名，从而为每个示例构建图像的代码："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "hct5EMEWNyfH"
      },
      "outputs": [],
      "source": [
        "import re\n",
        "\n",
        "def make_images(features):\n",
        "  image = [None]*400\n",
        "  new_feats = {}\n",
        "\n",
        "  for name, value in features.items():\n",
        "    match = re.match('r(\\d+)c(\\d+)', name)\n",
        "    if match:\n",
        "      image[int(match.group(1))*20+int(match.group(2))] = value\n",
        "    else:\n",
        "      new_feats[name] = value\n",
        "\n",
        "  image = tf.stack(image, axis=0)\n",
        "  image = tf.reshape(image, [20, 20, -1])\n",
        "  new_feats['image'] = image\n",
        "\n",
        "  return new_feats"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "61qy8utAwARP"
      },
      "source": [
        "将该函数应用于数据集中的每个批次："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "DJnnfIW9baE4"
      },
      "outputs": [],
      "source": [
        "fonts_image_ds = fonts_ds.map(make_images)\n",
        "\n",
        "for features in fonts_image_ds.take(1):\n",
        "  break"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ThqrthGwHSm"
      },
      "source": [
        "绘制生成的图像："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "I5dcey31T_tk",
        "outputId": "7fdaa853-5c71-46f0-9d02-890f16d10c1c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from matplotlib import pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(6,6), dpi=120)\n",
        "\n",
        "for n in range(9):\n",
        "  plt.subplot(3,3,n+1)\n",
        "  plt.imshow(features['image'][..., n])\n",
        "  plt.title(chr(features['m_label'][n]))\n",
        "  plt.axis('off')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7-nNR0Nncdd1"
      },
      "source": [
        "## 低级函数"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3jiGZeUijJNd"
      },
      "source": [
        "到目前为止，本教程重点介绍了用于读取 CSV 数据的最高级别效用函数。如果您的用例不符合基本模式，还有其他两个 API 可能对高级用户有所帮助。\n",
        "\n",
        "- `tf.io.decode_csv`：用于将文本行解析为 CSV 列张量列表的函数。\n",
        "- `tf.data.experimental.CsvDataset`：较低级别的 CSV 数据集构造函数。\n",
        "\n",
        "本部分会重新创建 `tf.data.experimental.make_csv_dataset` 提供的功能，以演示如何使用此较低级别的功能。\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LL_ixywomOHW"
      },
      "source": [
        "### `tf.io.decode_csv`\n",
        "\n",
        "此函数会将字符串或字符串列表解码为列列表。\n",
        "\n",
        "与 `tf.data.experimental.make_csv_dataset` 不同，此函数不会尝试猜测列数据类型。您可以通过为每列提供包含正确类型值的记录 `record_defaults` 值列表来指定列类型。\n",
        "\n",
        "要使用 <code>tf.io.decode_csv</code> 将 Titanic 数据<strong>作为字符串</strong>读取，您可以使用以下代码："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "m1D2C-qdlqeW",
        "outputId": "42d9faf1-7f8b-4d3d-c9d0-10805341fba2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['', '', '', '', '', '', '', '', '', '']"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "text = pathlib.Path(titanic_file_path).read_text()\n",
        "lines = text.split('\\n')[1:-1]\n",
        "\n",
        "all_strings = [str()]*10\n",
        "all_strings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "9W4UeJYyHPx5",
        "outputId": "526ba116-30a7-48b0-ee87-66cb11fd4b1c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "type: string, shape: (627,)\n",
            "type: string, shape: (627,)\n",
            "type: string, shape: (627,)\n",
            "type: string, shape: (627,)\n",
            "type: string, shape: (627,)\n",
            "type: string, shape: (627,)\n",
            "type: string, shape: (627,)\n",
            "type: string, shape: (627,)\n",
            "type: string, shape: (627,)\n",
            "type: string, shape: (627,)\n"
          ]
        }
      ],
      "source": [
        "features = tf.io.decode_csv(lines, record_defaults=all_strings)\n",
        "\n",
        "for f in features:\n",
        "  print(f\"type: {f.dtype.name}, shape: {f.shape}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j8TaHSQFoQL4"
      },
      "source": [
        "要使用实际类型解析它们，请创建相应类型的 `record_defaults` 列表："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "rzUjR59yoUe1",
        "outputId": "29ddd305-3d05-449f-9591-ad7599c4ca71",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0,male,22.0,1,0,7.25,Third,unknown,Southampton,n\n"
          ]
        }
      ],
      "source": [
        "print(lines[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "7sPTunxwoeWU",
        "outputId": "b6850d8f-067d-4117-89cd-3b32ef22986c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, '', 0.0, 0, 0, 0.0, '', '', '', '']"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "source": [
        "titanic_types = [int(), str(), float(), int(), int(), float(), str(), str(), str(), str()]\n",
        "titanic_types"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "n3NlViCzoB7F",
        "outputId": "7ee0cb81-5026-4ce8-e483-67dcc319317e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "type: int32, shape: (627,)\n",
            "type: string, shape: (627,)\n",
            "type: float32, shape: (627,)\n",
            "type: int32, shape: (627,)\n",
            "type: int32, shape: (627,)\n",
            "type: float32, shape: (627,)\n",
            "type: string, shape: (627,)\n",
            "type: string, shape: (627,)\n",
            "type: string, shape: (627,)\n",
            "type: string, shape: (627,)\n"
          ]
        }
      ],
      "source": [
        "features = tf.io.decode_csv(lines, record_defaults=titanic_types)\n",
        "\n",
        "for f in features:\n",
        "  print(f\"type: {f.dtype.name}, shape: {f.shape}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m-LkTUTnpn2P"
      },
      "source": [
        "注：在大批量行上调用 `tf.io.decode_csv` 比在单个 CSV 文本行上调用更有效。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yp1UItJmqGqw"
      },
      "source": [
        "### `tf.data.experimental.CsvDataset`\n",
        "\n",
        "`tf.data.experimental.CsvDataset` 类提供了一个最小的 CSV `Dataset` 接口，没有 `tf.data.experimental.make_csv_dataset` 函数的便利功能：列标题解析、列类型推断、自动重排、文件交错。\n",
        "\n",
        "此构造函数使用 `record_defaults` 的方式与 `tf.io.decode_csv` 相同：\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "9OzZLp3krP-t",
        "outputId": "2f517478-e1c6-4715-d6b9-2ee1689b663e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0, b'male', 22.0, 1, 0, 7.25, b'Third', b'unknown', b'Southampton', b'n']\n"
          ]
        }
      ],
      "source": [
        "simple_titanic = tf.data.experimental.CsvDataset(titanic_file_path, record_defaults=titanic_types, header=True)\n",
        "\n",
        "for example in simple_titanic.take(1):\n",
        "  print([e.numpy() for e in example])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_HBmfI-Ks7dw"
      },
      "source": [
        "上面的代码基本等价于："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "E5O5d69Yq7gG",
        "outputId": "519fc0dd-07fe-4bbd-f3af-c64f33919d1a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0, b'male', 22.0, 1, 0, 7.25, b'Third', b'unknown', b'Southampton', b'n']\n"
          ]
        }
      ],
      "source": [
        "def decode_titanic_line(line):\n",
        "  return tf.io.decode_csv(line, titanic_types)\n",
        "\n",
        "manual_titanic = (\n",
        "    # Load the lines of text\n",
        "    tf.data.TextLineDataset(titanic_file_path)\n",
        "    # Skip the header row.\n",
        "    .skip(1)\n",
        "    # Decode the line.\n",
        "    .map(decode_titanic_line)\n",
        ")\n",
        "\n",
        "for example in manual_titanic.take(1):\n",
        "  print([e.numpy() for e in example])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5R3ralsnt2AC"
      },
      "source": [
        "#### 多个文件\n",
        "\n",
        "要使用 `tf.data.experimental.CsvDataset` 解析字体数据集，您首先需要确定 `record_defaults` 的列类型。首先检查一个文件的第一行："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "3tlFOTjCvAI5",
        "outputId": "abf740ae-772b-496b-9419-1f3014666c54",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AGENCY,AGENCY FB,64258,0.400000,0,0.000000,35,21,51,22,20,20,1,1,1,21,101,210,255,255,255,255,255,255,255,255,255,255,255,255,255,255,1,1,1,93,255,255,255,176,146,146,146,146,146,146,146,146,216,255,255,255,1,1,1,93,255,255,255,70,1,1,1,1,1,1,1,1,163,255,255,255,1,1,1,93,255,255,255,70,1,1,1,1,1,1,1,1,163,255,255,255,1,1,1,93,255,255,255,70,1,1,1,1,1,1,1,1,163,255,255,255,1,1,1,93,255,255,255,70,1,1,1,1,1,1,1,1,163,255,255,255,1,1,1,93,255,255,255,70,1,1,1,1,1,1,1,1,163,255,255,255,141,141,141,182,255,255,255,172,141,141,141,115,1,1,1,1,163,255,255,255,255,255,255,255,255,255,255,255,255,255,255,209,1,1,1,1,163,255,255,255,6,6,6,96,255,255,255,74,6,6,6,5,1,1,1,1,163,255,255,255,1,1,1,93,255,255,255,70,1,1,1,1,1,1,1,1,163,255,255,255,1,1,1,93,255,255,255,70,1,1,1,1,1,1,1,1,163,255,255,255,1,1,1,93,255,255,255,70,1,1,1,1,1,1,1,1,163,255,255,255,1,1,1,93,255,255,255,70,1,1,1,1,1,1,1,1,163,255,255,255,1,1,1,93,255,255,255,70,1,1,1,1,1,1,1,1,163,255,255,255,1,1,1,93,255,255,255,70,1,1,1,1,1,1,1,1,163,255,255,255,1,1,1,93,255,255,255,70,1,1,1,1,1,1,1,1,163,255,255,255,1,1,1,93,255,255,255,70,1,1,1,1,1,1,1,1,163,255,255,255,1,1,1,93,255,255,255,70,1,1,1,1,1,1,1,1,163,255,255,255,1,1,1,93,255,255,255,70,1,1,1,1,1,1,1,1,163,255,255,255\n"
          ]
        }
      ],
      "source": [
        "font_line = pathlib.Path(font_csvs[0]).read_text().splitlines()[1]\n",
        "print(font_line)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "etyGu8K_ySRz"
      },
      "source": [
        "只有前两个字段是字符串，其余的都是整数或浮点数，通过计算逗号的个数可以得到特征总数："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "crgZZn0BzkSB"
      },
      "outputs": [],
      "source": [
        "num_font_features = font_line.count(',')+1\n",
        "font_column_types = [str(), str()] + [float()]*(num_font_features-2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YeK2Pw540RNj"
      },
      "source": [
        "`tf.data.experimental.CsvDataset` 构造函数可以获取输入文件列表，但会按顺序读取它们。CSV 列表中的第一个文件是 `AGENCY.csv`："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "_SvL5Uvl0r0N",
        "outputId": "3f16fa2a-2735-49ef-9890-a0588e5729b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'fonts/AGENCY.csv'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "font_csvs[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EfAX3G8Xywy6"
      },
      "source": [
        "因此，当您将文件列表传递给 `CsvDataset` 时，会首先读取 `AGENCY.csv` 中的记录："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "Gtr1E66VmBqj"
      },
      "outputs": [],
      "source": [
        "simple_font_ds = tf.data.experimental.CsvDataset(\n",
        "    font_csvs,\n",
        "    record_defaults=font_column_types,\n",
        "    header=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "k750Mgq4yt_o",
        "outputId": "cfbb9042-0615-4fff-d81a-ce4d6a9d3a01",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "b'AGENCY'\n",
            "b'AGENCY'\n",
            "b'AGENCY'\n",
            "b'AGENCY'\n",
            "b'AGENCY'\n",
            "b'AGENCY'\n",
            "b'AGENCY'\n",
            "b'AGENCY'\n",
            "b'AGENCY'\n",
            "b'AGENCY'\n"
          ]
        }
      ],
      "source": [
        "for row in simple_font_ds.take(10):\n",
        "  print(row[0].numpy())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NiqWKQV21FrE"
      },
      "source": [
        "要交错多个文件，请使用 `Dataset.interleave`。\n",
        "\n",
        "这是一个包含 CSV 文件名的初始数据集："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "t9dS3SNb23W8"
      },
      "outputs": [],
      "source": [
        "font_files = tf.data.Dataset.list_files(\"fonts/*.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TNiLHMXpzHy5"
      },
      "source": [
        "这会在每个周期重排文件名："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "id": "zNd-TYyNzIgg",
        "outputId": "293e4853-1b1a-498a-be27-0449a9424cb6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1:\n",
            "     b'fonts/ARIAL.csv'\n",
            "     b'fonts/SNAP.csv'\n",
            "     b'fonts/SKETCHFLOW.csv'\n",
            "     b'fonts/SCRIPT.csv'\n",
            "     b'fonts/HARRINGTON.csv'\n",
            "    ...\n",
            "\n",
            "Epoch 2:\n",
            "     b'fonts/ISOC.csv'\n",
            "     b'fonts/VINETA.csv'\n",
            "     b'fonts/CURLZ.csv'\n",
            "     b'fonts/PERPETUA.csv'\n",
            "     b'fonts/SYLFAEN.csv'\n",
            "    ...\n"
          ]
        }
      ],
      "source": [
        "print('Epoch 1:')\n",
        "for f in list(font_files)[:5]:\n",
        "  print(\"    \", f.numpy())\n",
        "print('    ...')\n",
        "print()\n",
        "\n",
        "print('Epoch 2:')\n",
        "for f in list(font_files)[:5]:\n",
        "  print(\"    \", f.numpy())\n",
        "print('    ...')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B0QB1PtU3WAN"
      },
      "source": [
        "`interleave` 方法采用 `map_func`，它会为父 `Dataset`的每个元素创建一个子 `Dataset`。\n",
        "\n",
        "在这里，您要从文件数据集的每个元素创建一个 `tf.data.experimental.CsvDataset`："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "QWp4rH0Q4uPh"
      },
      "outputs": [],
      "source": [
        "def make_font_csv_ds(path):\n",
        "  return tf.data.experimental.CsvDataset(\n",
        "    path,\n",
        "    record_defaults=font_column_types,\n",
        "    header=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VxRGdLMB5nRF"
      },
      "source": [
        "交错返回的 `Dataset` 通过循环遍历多个子 `Dataset` 来返回元素。请注意，下面的数据集如何在 `cycle_length=3` 三个字体文件中循环："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "id": "OePMNF_x1_Cc"
      },
      "outputs": [],
      "source": [
        "font_rows = font_files.interleave(make_font_csv_ds,\n",
        "                                  cycle_length=3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "id": "UORIGWLy54-E",
        "outputId": "d23f854b-5b4b-4557-bf9b-67119d9e05ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        }
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "'numpy.float32' object cannot be interpreted as an integer",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-73-06da9fcfb5e9>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfont_rows\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m   \u001b[0mfonts_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'font_name'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m   \u001b[0mfonts_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'character'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfonts_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: 'numpy.float32' object cannot be interpreted as an integer"
          ]
        }
      ],
      "source": [
        "fonts_dict = {'font_name':[], 'character':[]}\n",
        "\n",
        "for row in font_rows.take(10):\n",
        "  fonts_dict['font_name'].append(row[0].numpy().decode())\n",
        "  fonts_dict['character'].append(chr(row[2].numpy()))\n",
        "\n",
        "pd.DataFrame(fonts_dict)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mkKZa_HX8zAm"
      },
      "source": [
        "#### 性能\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8BtGHraUApdJ"
      },
      "source": [
        "早些时候，有人注意到 `tf.io.decode_csv` 在一个批次字符串上运行时效率更高。\n",
        "\n",
        "当使用大批次时，可以利用这一事实来提高 CSV 加载性能（但请先尝试使用[缓存](#caching)）。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d35zWMH7MDL1"
      },
      "source": [
        "使用内置加载器 20，2048 个样本批次大约需要 17 秒。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "id": "ieUVAPryjpJS"
      },
      "outputs": [],
      "source": [
        "BATCH_SIZE=2048\n",
        "fonts_ds = tf.data.experimental.make_csv_dataset(\n",
        "    file_pattern = \"fonts/*.csv\",\n",
        "    batch_size=BATCH_SIZE, num_epochs=1,\n",
        "    num_parallel_reads=100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "MUC2KW4LkQIz",
        "outputId": "64312a94-45b1-46af-8a45-aeccda646052",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "....................\n",
            "CPU times: user 35 s, sys: 4.13 s, total: 39.1 s\n",
            "Wall time: 31.2 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "for i,batch in enumerate(fonts_ds.take(20)):\n",
        "  print('.',end='')\n",
        "\n",
        "print()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5lhnh6rZEDS2"
      },
      "source": [
        "将**批量文本行**传递给 `decode_csv` 运行速度更快，大约需要 5 秒："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "id": "4XbPZV1okVF9"
      },
      "outputs": [],
      "source": [
        "fonts_files = tf.data.Dataset.list_files(\"fonts/*.csv\")\n",
        "fonts_lines = fonts_files.interleave(\n",
        "    lambda fname:tf.data.TextLineDataset(fname).skip(1),\n",
        "    cycle_length=100).batch(BATCH_SIZE)\n",
        "\n",
        "fonts_fast = fonts_lines.map(lambda x: tf.io.decode_csv(x, record_defaults=font_column_types))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "id": "te9C2km-qO8W",
        "outputId": "57c56802-3d1b-4bf7-9ffb-4c9a44e13845",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "....................\n",
            "CPU times: user 4.88 s, sys: 89.2 ms, total: 4.97 s\n",
            "Wall time: 4.16 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "for i,batch in enumerate(fonts_fast.take(20)):\n",
        "  print('.',end='')\n",
        "\n",
        "print()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aebC1plsMeOi"
      },
      "source": [
        "有关通过使用大批次提高 CSV 性能的另一个示例，请参阅[过拟合和欠拟合教程](../keras/overfit_and_underfit.ipynb)。\n",
        "\n",
        "这种方式可能有效，但请考虑其他选项，例如 `Dataset.cache` 和 `tf.data.Dataset.snapshot`，或者将您的数据重新编码为更简化的格式。"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "csv.ipynb",
      "toc_visible": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}